Question: Utilizing temporal coherence and motion-attention for predicting facial attributes in video.

This paper explores an enhanced method for visual learning from unlabeled video content, moving beyond traditional "slow" feature analysis. Existing techniques focus on minimizing the differences in representations between temporally adjacent frames, capturing the gradual nature of high-level visual signals over time. However, they overlook the dynamics of visual content transformation. To address this, we introduce "steady" feature analysis, a refinement that imposes a prior ensuring that higher order derivatives within the feature space remain minimal. This is achieved by training a convolutional neural network with a regularization applied to sequences of frames from unlabeled videos, promoting smooth transitions in feature changes that mirror recent alterations. Through experiments on a variety of datasets, including YouTube and KITTI videos, we showcase the effectiveness of our method in tasks related to object, scene, and action recognition. Remarkably, our features derived from unlabeled video outperform those obtained through a conventional, heavily supervised pretraining approach, highlighting the potential of leveraging temporal coherence for visual learning enhancement.

This thesis focuses on the area of face processing and aims at designing a reliable framework to facilitate face, age, and gender recognition. A Bag-of-Words framework has been optimized for the task of face recognition by evaluating different feature descriptors and different bag-of-words configurations. More specifically, we choose a compact set of features (e.g., descriptors, window locations, window sizes, dictionary sizes, etc.) in order to produce the highest possible rate of accuracy. Experiments on a challenging dataset shows that our framework achieves a better level of accuracy when compared to other popular approaches such as dimension reduction techniques, edge detection operators, and texture and shape feature extractors. The second contribution of this thesis is the proposition of a general framework for age and gender classification. Although the vast majority of the existing solutions focus on a single visual descriptor that often only encodes a certain characteristic of the image regions, this thesis aims at integrating multiple feature types. For this purpose, feature selection is employed to obtain more accurate and robust facial descriptors. Once descriptors have been computed, a compact set of features is chosen, which facilitates facial image processing for age and gender analysis. In addition to this, a new color descriptor (CLR-LBP) is proposed and the results obtained is shown to be comparable to those of other pre-existing color descriptors. The experimental results indicates that our age and gender framework outperforms other proposed methods when examined on two challenging databases, where face objects are present with different expressions and levels of illumination. This achievement demonstrates the effectiveness of our proposed solution and allows us to achieve a higher accuracy over the existing state-of-the-art methods.

We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second.

The human visual system appears to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. While the mechanisms in the human visual system that do this are unknown, the effect is not too surprising given that temporally adjacent frames in a video sequence contain slightly different, but unique, information. This paper addresses the use of both the spatial and temporal information present in a short image sequence to create a single high-resolution video frame. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with a discontinuity-preserving prior image model is used to extract a high-resolution video still given a short low-resolution sequence. Estimates computed from a low-resolution image sequence containing a subpixel camera pan show dramatic visual and quantitative improvements over bilinear, cubic B-spline, and Bayesian single frame interpolations. Visual and quantitative improvements are also shown for an image sequence containing objects moving with independent trajectories. Finally, the video frame extraction algorithm is used for the motion-compensated scan conversion of interlaced video data, with a visual comparison to the resolution enhancement obtained from progressively scanned frames.

Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.

Human interaction prediction, i.e., the recognition of an ongoing interaction activity before it is completely executed, has a wide range of applications such as human-robot interaction and the prevention of dangerous events. Due to the large variations in appearance and the evolution of scenes, interaction prediction at an early stage is a challenging task. In this paper, a novel structural feature is exploited as a complement together with the spatial and temporal information to improve the performance of interaction prediction. The proposed structural feature is captured by Long Short Term Memory (LSTM) networks, which process the global and local features associated to each frame and each optical flow image. A new ranking score fusion method is then introduced to combine the spatial, temporal and structural models. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for human interaction prediction on the BIT-Interaction, the TV Human Interaction and the UT-Interaction datasets.

We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face images, without any explicit part supervision. The observation motivates a new method for finding faces through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is data-driven, and carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variations. Our method achieves promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW, and WIDER FACE.

We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor’s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character.

A fundamental task in artificial intelligence and computer vision is to build machines that can behave like a human in recognizing a broad range of visual concepts. This paper aims to investigate and develop intelligent systems for learning the concept of female facial beauty and producing human-like predictors. Artists and social scientists have long been fascinated by the notion of facial beauty, but study by computer scientists has only begun in the last few years. Our work is notably different from and goes beyond previous works in several aspects: 1) we focus on fully-automatic learning approaches that do not require costly manual annotation of landmark facial features but simply take the raw pixels as inputs; 2) our study is based on a collection of data that is an order of magnitude larger than that of any previous study; 3) we imposed no restrictions in terms of pose, lighting, background, expression, age, and ethnicity on the face images used for training and testing. These factors significantly increased the difficulty of the learning task. We show that a biologically-inspired model with multiple layers of trainable feature extractors can produce results that are much more human-like than the previously used eigenface approach. Finally, we develop a novel visualization method to interpret the learned model and revealed the existence of several beautiful features that go beyond the current averageness and symmetry hypotheses.

Humans modify facial expressions in order to mislead observers regarding their true emotional states. Being able to recognize the authenticity of emotional displays is notoriously difficult for human observers. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of genuine and deceptive facial expressions of emotions for automatic recognition1 . We show that overall the problem of recognizing deceptive facial expressions can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt feature space. Interesting additional results show that on average it is easier to distinguish among genuine expressions than deceptive ones and that certain emotion pairs are more difficult to distinguish than others.

Security and safety applications such as surveillance or forensics demand face recognition in low-resolution video data. We propose a face recognition method based on a Convolutional Neural Network (CNN) with a manifold-based track comparison strategy for low-resolution video face recognition. The low-resolution domain is addressed by adjusting the network architecture to prevent bottlenecks or significant upscaling of face images. The CNN is trained with a combination of a large-scale self-collected video face dataset and large-scale public image face datasets resulting in about 1.4M training images. To handle large amounts of video data and for effective comparison, the CNN face descriptors are compared efficiently on track level by local patch means. Our setup achieves 80.3 percent accuracy on a 32×32 pixels low-resolution version of the YouTube Faces Database and outperforms local image descriptors as well as the state-of-the-art VGG-Face network [20] in this domain. The superior performance of the proposed method is confirmed on a self-collected in-the-wild surveillance dataset.


Question: Forecasting facial features in video through temporal consistency and motion-attention mechanisms.

This comprehensive survey examines the advancements and challenges in the field of face recognition, an area that has garnered considerable interest due to its potential in commercial and law enforcement applications. The growing attention to face recognition can be attributed to two primary factors: the expanding array of practical applications and the maturation of underlying technologies following decades of research. Despite the progress, current automated face recognition systems are constrained by the complexities introduced by real-world scenarios, such as varying illumination conditions and changes in pose, particularly when dealing with outdoor environments. The human visual system's superior capability in handling such variables remains a benchmark for machine recognition systems to aspire to.

The paper offers an exhaustive and critical review of the literature on face recognition from both still images and video sequences. The authors' dual motivation for this survey is to provide a contemporary analysis of existing research and to contribute insights into the field of machine recognition of faces. To achieve a comprehensive coverage, the survey categorizes and discusses various recognition techniques, detailing illustrative methods within each category. Additionally, the paper delves into related areas, including psychophysical studies, system evaluation methodologies, and the critical issues surrounding illumination and pose variations, which significantly impact the performance of face recognition systems.

In the context of video-based face recognition, the survey highlights the importance of temporal coherence and motion-attention mechanisms. Temporal coherence refers to the consistency of facial attributes across consecutive frames, which can enhance the accuracy of recognition by leveraging the temporal context. Motion-attention, on the other hand, involves focusing on regions of the video where facial movements are most pronounced, which can aid in distinguishing between different individuals by their unique facial dynamics. These techniques are crucial for improving the robustness of face recognition systems in video environments, where facial expressions and head movements are more dynamic compared to static images.

The survey concludes by acknowledging the progress made in face recognition technology but also underscores the ongoing need for research to address the limitations in real-world performance. It emphasizes the significance of developing methods that can handle challenging conditions, such as variations in lighting and pose, to bridge the gap between current systems and the capabilities of human perception. The authors also suggest areas for future investigation, including the integration of temporal coherence and motion-attention with other recognition techniques, to further enhance the reliability and accuracy of face recognition systems in practical applications.

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets SomethingSomething, Jester, and Charades which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.

Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.

Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.

In this work, we present an approach for segmenting objects in videos taken in complex scenes. It propagates initial object label through the entire video by a frame-sequential manner where the initial label is usually given by the user. The proposed method has several contributions which make the propagation much more robust and accurate than other methods. First, a novel supervised motion estimation algorithm is employed between each pair of neighboring frames, by which a predicted shape model can be warped in order to segment the similar color around object boundary. Second, unlike previous methods with fixed modeling range, we design a novel range-adaptive appearance model to handle the tough problem of occlusion. Last, the paper gives a reasonable framework based on GraphCut algorithm for obtaining the final label of the object by combining the clues from both appearance and motion. In the experiments, the proposed approach is evaluated qualitatively and quantitatively with some recent methods to show it achieves state-of-art results on multiple videos from benchmark data sets.

Humans detect and identify faces in a scene with little or no effort . However, building an automated system that accomplishes this task is very difficult . There are several related Subproblems : detection of a pattern as a face . identification of the face, analysis of facial expressions, and classification based on physical features of the face . A system that performs these operations will find many applications, e .g . criminal identification, authentication in secure systems, etc . Most of the work to date has been in identification . This paper surveys the past work in solving these problems . The capability of the human visual system with respect to these problems is also discussed . It is meant to serve as a guide for an automated system . Some new approaches to these problems are also briefly discussed . Face detection Face identification Facial expressions Classification Facial features

We describe new techniques to detect and analyze periodic mo t on as seen from both a static and moving camera. By tracking objects of interest, we compute a n object’s self-similarity as it evolves in time. For periodic motion, the self-similarity measure is a l o periodic, and we apply Time-Frequency analysis to detect and characterize the periodic motion. Th e periodicity is also analyzed robustly using the 2-D lattice structures inherent in similarity matrices . A real-time system has been implemented to track and classify objects using periodicity. Examples of o bject classification (people, running dogs, vehicles), person counting, and non-stationary periodici ty are provided.

This paper presents a study in which driver's gaze zone is categorized using new deep learning techniques. Since the sequence of gaze zones of a driver reflects precisely what and how he behaves, it allows us infer his drowsiness, focusing or distraction by analyzing the images coming from a camera. A Haar feature based face detector is combined with a correlation filter based MOSS tracker for the face detection task to handle a tough visual environment in the car. Driving database is a big-data which was constructed using a recording setup within a compact sedan by driving around the urban area. The gaze zones consist of 9 categories depending on where a driver is looking at during driving. A convolutional neural network is trained to categorize driver's gaze zone from a given face detected image using a multi-GPU platform, and then its network parameters are transferred to a GPU within a PC running on Windows to operate in the real-time basis. Result suggests that the correct rate of gaze zone categorization reaches to 95% in average, indicating that our system outperforms the state-of-art gaze zone categorization methods based on conventional computer vision techniques.

Not all frames are equal – selecting a subset of discriminative frames from a video can improve performance at detecting and recognizing human interactions. In this paper we present models for categorizing a video into one of a number of predefined interactions or for detecting these interactions in a long video sequence. The models represent the interaction by a set of key temporal moments and the spatial structures they entail. For instance: two people approaching each other, then extending their hands before engaging in a “handshaking” interaction. Learning the model parameters requires only weak supervision in the form of an overall label for the interaction. Experimental results on the UT-Interaction and VIRAT datasets verify the efficacy of these structured models for human interactions.

Given a photo of person A, we seek a photo of person B with similar pose and expression. Solving this problem enables a form of puppetry, in which one person appears to control the face of another. When deployed on a webcam-equipped computer, our approach enables a user to control another person’s face in real-time. This image-retrievalinspired approach employs a fully-automated pipeline of face analysis techniques, and is extremely general—we can puppet anyone directly from their photo collection or videos in which they appear. We show several examples using images and videos of celebrities from the Internet.


Question: Forecasting facial features in video through temporal consistency and motion-attention mechanisms.

This paper addresses the significant challenge of facial recognition in unconstrained video footage, a task distinct from, yet related to, face recognition in static images. Given the growing importance of this issue, various methods have been proposed, and benchmark datasets have been compiled to aid in its study. However, a significant discrepancy exists between the demands of practical applications and the current technological capabilities. Our contributions are threefold: (a) We introduce a comprehensive database, labeled as 'YouTube Faces', featuring challenging, uncontrolled conditions for facial recognition, accompanied by benchmark pair-matching tests. (b) Using this benchmark, we conduct a thorough evaluation and comparison of a wide range of existing facial recognition techniques in video format. (c) We propose a novel similarity measure, Matched Background Similarity (MBGS), designed specifically for set-to-set comparisons. This measure is demonstrated to significantly enhance performance in benchmark tests, thereby bridging the gap between theoretical advancements and real-world application needs in the realm of facial recognition in unconstrained videos.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

In this paper, we study the problem of video-based person re-identification. This is more challenging, and of greater practical interest, than conventional image-based person re-identification. To address this problem, we propose the use of convolutional Long Short Term Memory (LSTM) based networks to learn a video-based representation for person re-identification. To this end, we propose to jointly leverage deep Convolutional Neural Networks (CNNs) and LSTM networks. Given sequential video frames of a person, the spatial information encoded in the frames is first extracted by a set of CNNs. An encoderdecoder framework derived from LSTMs is employed to encode the resulting temporal of CNN outputs. This approach leads to a refined feature representation that is able to explicitly model the video as an ordered sequence, while preserving the spatial information. Comparative experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose.

Humans detect and identify faces in a scene with little or no effort . However, building an automated system that accomplishes this task is very difficult . There are several related Subproblems : detection of a pattern as a face . identification of the face, analysis of facial expressions, and classification based on physical features of the face . A system that performs these operations will find many applications, e .g . criminal identification, authentication in secure systems, etc . Most of the work to date has been in identification . This paper surveys the past work in solving these problems . The capability of the human visual system with respect to these problems is also discussed . It is meant to serve as a guide for an automated system . Some new approaches to these problems are also briefly discussed . Face detection Face identification Facial expressions Classification Facial features

This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.

Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks. Previous modelbased and deep learning approaches achieve satisfactory performance for 2D face spoofs, but remain limited for more advanced 3D attacks such as vivid masks. In this paper, we address 3D face anti-spoofing via the proposed Hypergraph Convolutional Neural Networks (HGCNN). Firstly, we construct a computation-efficient and posture-invariant face representation with only a few key points on hypergraphs. The hypergraph representation is then fed into the designed HGCNN with hypergraph convolution for feature extraction, while the depth auxiliary is also exploited for 3D mask anti-spoofing. Further, we build a 3D face attack database with color, depth and infrared light information to overcome the deficiency of 3D face anti-spoofing data. Experiments show that our method achieves the state-of-theart performance over widely used 3D and 2D databases as well as the proposed one under various tests.

This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.

Speech-driven facial motion synthesis is a well explored research topic. However, little has been done to model expressive visual behavior during speech. We address this issue using a machine learning approach that relies on a database of speech-related high-fidelity facial motions. From this training set, we derive a generative model of expressive facial motion that incorporates emotion control, while maintaining accurate lip-synching. The emotional content of the input speech can be manually specified by the user or automatically extracted from the audio signal using a Support Vector Machine classifier.

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods.

Facial action units provide an objective characterization of facial muscle movements. Automatic estimation of facial action unit intensities is a challenging problem given individual differences in neutral face appearances and the need to generalize across different pose, illumination and datasets. In this paper, we introduce the Local-Global Ranking method as a novel alternative to direct prediction of facial action unit intensities. Our method takes advantage of the additional information present in videos and image collections of the same person (e.g. a photo album). Instead of trying to estimate facial expression intensities independently for each image, our proposed method performs a two-stage ranking: a local pair-wise ranking followed by a global ranking. The local ranking is designed to be accurate and robust by making a simple 3-class comparison (higher, equal, or lower) between randomly sampled pairs of images. We use a Bayesian model to integrate all these pair-wise rankings and construct a global ranking. Our Local-Global Ranking method shows state-of-the-art performance on two publicly-available datasets. Our cross-dataset experiments also show better generalizability.


Question: Forecasting facial features in video through temporal consistency and motion-attention.

Caffe: A Framework for Efficient Deep Learning and Multimedia Analysis

Caffe, a robust and versatile framework, has been developed to provide multimedia researchers and practitioners with a flexible platform for implementing cutting-edge deep learning algorithms. This open-source tool, licensed under the BSD license, is a C++ library with interfaces in Python and MATLAB, designed for the efficient training and deployment of convolutional neural networks and other deep models on standard hardware architectures. Caffe's performance is tailored to meet the demands of large-scale media processing, leveraging CUDA GPU computation to process over 40 million images per day on a single K40 or Titan GPU, achieving an impressive processing speed of approximately 2 milliseconds per image.

The framework's design philosophy emphasizes model representation independence from its implementation, enabling researchers to experiment freely and transition seamlessly between different platforms. This flexibility facilitates development and deployment, whether on local prototyping machines or in cloud environments, catering to both academic and industrial needs. Caffe, maintained and enhanced by the Berkeley Vision and Learning Center (BVLC) and a vibrant community of contributors on GitHub, is instrumental in driving innovation in vision, speech, and multimedia projects. It supports a wide array of applications, from cutting-edge research to large-scale industrial implementations and startup prototypes, demonstrating its versatility and impact in the field of deep learning and multimedia analysis.

Scripts provide rich textual annotation of movies, including dialogs, character names, and other situational descriptions. Exploiting such rich annotations requires aligning the sentences in the scripts with the corresponding video frames. Previous work on aligning movies with scripts predominantly relies on time-aligned closed-captions or subtitles, which are not always available. In this paper, we focus on automatically aligning faces in movies with their corresponding character names in scripts without requiring closed-captions/subtitles. We utilize the intuition that faces in a movie generally appear in the same sequential order as their names are mentioned in the script. We first apply standard techniques for face detection and tracking, and cluster similar face tracks together. Next, we apply a generative Hidden Markov Model (HMM) and a discriminative Latent Conditional Random Field (LCRF) to align the clusters of face tracks with the corresponding character names. Our alignment models (especially LCRF) significantly outperform the previous state-of-the-art on two different movie datasets and for a wide range of face clustering algorithms.

We describe how to create with machine learning techniques a generative, speech animation module. A human subject is first recorded using a videocamera as he/she utters a predetermined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth uttering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is videorealistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned.The two key contributions of this paper are 1) a variant of the multidimensional morphable model (MMM) to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes; and 2) a trajectory synthesis technique based on regularization, which is automatically trained from the recorded video corpus, and which is capable of synthesizing trajectories in MMM space corresponding to any desired utterance.

In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms.

We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We introduce a novel pixel-trajectory recurrent neural network that learns feature embeddings of foreground pixel trajectories linked in time. By clustering the pixel trajectories using the learned feature embeddings, our method establishes correspondences between foreground object masks across video frames. To demonstrate the effectiveness of our framework for object discovery, we conduct experiments on commonly used datasets for motion segmentation, where we achieve state-of-the-art performance.

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second.

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcherindependent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.

As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative expression of a query face compared to a reference face might alleviate this difficulty. In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose. The contrastive representation is calculated at the embedding layer of deep networks by comparing a given (query) image with the reference image. We attempt to utilize a generative reference image that is estimated based on the given image. Consequently, we deploy deep neural networks that embed a combination of a generative model, a contrastive model, and a discriminative model with an end-to-end training manner. In our proposed networks, we attempt to disentangle a facial expressive factor in two steps including learning of a generator network and a contrastive encoder network. We conducted extensive experiments on publicly available face expression databases (CK+, MMI, Oulu-CASIA, and in-the-wild databases) that have been widely adopted in the recent literatures. The proposed method outperforms the known state-of-the art methods in terms of the recognition accuracy.

This paper proposes a method for detecting facial regions by combining a Gabor filter and a convolutional neural network. The first stage uses the Gabor filter which extracts intrinsic facial features. As a result of this transformation we obtain four subimages. The second stage of the method concerns the application of the convolutional neural network to these four images. The approach presented in this paper yields better classification performance in comparison to the results obtained by the convolutional neural network alone.

In this paper, weinvestigaterecognition of humanfaces in a meetingroom. The major challenges of identifying humanfacesin this environmentincludelow quality of input images,poor illumination,unrestrictedheadposesand continuouslychangingfacial expressionsandocclusion.In order to addresstheseproblemswe proposea novel algorithm, DynamicSpaceWarping (DSW).Thebasic idea of the algorithm is to combinelocal features under certain spatial constraints. We compare DSWwith the eigenface approachondatacollectedfromvariousmeetings.Wehave testedboth front and profile face imagesand imageswith two stagesof occlusion.Theexperimentalresultsindicate thattheDSWapproachoutperformstheeigenfaceapproach in bothcases.


Question: Forecasting facial features in video through temporal consistency and motion-attention mechanism.

This paper focuses on the recognition of natural human actions within diverse and realistic video contexts, a subject that, despite its significance, has been largely overlooked due to the scarcity of appropriately annotated video datasets. Our primary contribution lies in addressing this gap by exploring the utilization of movie scripts to automatically annotate human actions in videos. We assess various approaches for action retrieval from scripts and highlight the advantages of employing a text-based classifier. Following this, we turn our attention to the task of action classification within videos. We introduce a novel method for video classification that builds upon and enhances recent concepts, including local space-time features, space-time pyramids, and multi-channel non-linear SVMs. Our method outperforms current state-of-the-art results on the widely used KTH action dataset, achieving an accuracy rate of 91.8%. Acknowledging the potential for noisy labels in automatically annotated datasets, we specifically examine and demonstrate the robustness of our method against annotation errors in the training set. Ultimately, we apply our method to the learning and classification of complex action classes within movies, yielding encouraging outcomes.

Computing optical flow between any pair of Internet face photos is challenging for most current state of the art flow estimation methods due to differences in illumination, pose, and geometry. We show that flow estimation can be dramatically improved by leveraging a large photo collection of the same (or similar) object. In particular, consider the case of photos of a celebrity from Google Image Search. Any two such photos may have different facial expression, lighting and face orientation. The key idea is that instead of computing flow directly between the input pair (I, J), we compute versions of the images (I', J') in which facial expressions and pose are normalized while lighting is preserved. This is achieved by iteratively projecting each photo onto an appearance subspace formed from the full photo collection. The desired flow is obtained through concatenation of flows (I → I') o (J' → J). Our approach can be used with any two-frame optical flow algorithm, and significantly boosts the performance of the algorithm by providing invariance to lighting and shape changes.

This paper presents a study in which driver's gaze zone is categorized using new deep learning techniques. Since the sequence of gaze zones of a driver reflects precisely what and how he behaves, it allows us infer his drowsiness, focusing or distraction by analyzing the images coming from a camera. A Haar feature based face detector is combined with a correlation filter based MOSS tracker for the face detection task to handle a tough visual environment in the car. Driving database is a big-data which was constructed using a recording setup within a compact sedan by driving around the urban area. The gaze zones consist of 9 categories depending on where a driver is looking at during driving. A convolutional neural network is trained to categorize driver's gaze zone from a given face detected image using a multi-GPU platform, and then its network parameters are transferred to a GPU within a PC running on Windows to operate in the real-time basis. Result suggests that the correct rate of gaze zone categorization reaches to 95% in average, indicating that our system outperforms the state-of-art gaze zone categorization methods based on conventional computer vision techniques.

Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.

This paper describes a system for creating a photo-realistic model of the human head that can be animated and lip-synched from phonetic transcripts of text. Combined with a state-of-the-art text-to-speech synthesizer (TTS), it generates video animations of talking heads that closely resemble real people. To obtain a naturally looking head, we choose a “data-driven” approach. We record a talking person and apply image recognition to extract automatically bitmaps of facial parts. These bitmaps are normalized and parameterized before being entered into a database. For synthesis, the TTS provides the audio track, as well as the phonetic transcript from which trajectories in the space of parameterized bitmaps are computed for all facial parts. Sampling these trajectories and retrieving the corresponding bitmaps from the database produces animated facial parts. These facial parts are then projected and blended onto an image of the whole head using its pose information. This talking head model can produce new, never recorded speech of the person who was originally recorded. Talking-head animations of this type are useful as a front-end for agents and avatars in multimedia applications such as virtual operators, virtual announcers, help desks, educational, and expert systems.

In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.

This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.

Recent GAN-based video generation approaches model videos as the combination of a time-independent scene component and a time-varying motion component, thus factorizing the generation problem into generating background and foreground separately. One of the main limitations of current approaches is that both factors are learned by mapping one source latent space to videos, which complicates the generation task as a single data point must be informative of both background and foreground content. In this paper we propose a GAN framework for video generation that, instead, employs two latent spaces in order to structure the generative process in a more natural way: 1) a latent space to generate the static visual content of a scene (background), which remains the same for the whole video, and 2) a latent space where motion is encoded as a trajectory between sampled points and whose dynamics are modeled through an RNN encoder (jointly trained with the generator and the discriminator) and then mapped by the generator to visual objects’ motion. Additionally, we extend current video discrimination approaches by incorporating in the learning procedure motion estimation and, leveraging the peculiarity of the generation process, unsupervised pixel-wise dense predictions. Extensive performance evaluation showed that our approach is able to a) synthesize more realistic videos than state-of-the-art methods, b) learn effectively both local and global video dynamics, as demonstrated by the results achieved on a video action recognition task over the UCF-101 dataset, and c) accurately perform unsupervised video object segmentation on standard video benchmarks, such as DAVIS, SegTrack and F4K-Fish.

Optical flow provides a constraint on the motion of a deformable model. We derive and solve a dynamic system incorporating flow as a hard constraint, producing a model-based least-squares optical flow solution. Our solution also ensures the constraint remains satisfied when combined with edge information, which helps combat tracking error accumulation. Constraint enforcement can be relaxed using a Kalman filter, which permits controlled constraint violations based on the noise present in the optical flow information, and enables optical flow and edge information to be combined more robustly and efficiently. We apply this framework to the estimation of face shape and motion using a 3D deformable face model. This model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. We present experiments in extracting the shape and motion of a face from image sequences which validate the accuracy of the method. They also demonstrate that our treatment of optical flow as a hard constraint, as well as our use of a Kalman filter to reconcile these constraints with the uncertainty in the optical flow, are vital for improving the performance of our system.

Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

In this paper, we address the problem of extreme head pose estimation from intensity images, in a monocular setup. We introduce a novel fusion pipeline to integrate into a dedicated Kalman Filter the pose estimated from a tracking scheme in the prediction stage and the pose estimated from a detection scheme in the correction stage. To that end, the measurement covariance of the Kalman Filter is updated in every frame. The tracking scheme is performed using a set of keypoints extracted in the area of the head along with a simple 3D geometric model. The detection scheme, on the other hand, relies on the alignment of facial landmarks in each frame combined with 3D features extracted on a head mesh. The head pose in each scheme is estimated by minimizing the reprojection error from the 3D-2D correspondences. By combining both frameworks, we extend the applicability of head pose estimation from facial landmarks to cases where these features are no longer visible. We compared the proposed method to other related approaches, showing that it can achieve state-of-the-art performance. We also demonstrate that our approach is suitable for cases with extreme head rotations and (self-) occlusions, besides being suitable for real time applications.


Question: Cooperative learning techniques applied to deep neural network architectures.

This paper introduces a novel method for enhancing the performance of deep neural networks in semi-supervised learning scenarios, where only a fraction of the training dataset is labeled. The approach, termed Temporal Ensembling, involves creating a consensus prediction for unlabeled data by aggregating the outputs of the network at various training epochs, under diverse regularization techniques and input augmentation conditions. This ensemble prediction is considered a more reliable estimator of the unknown labels than the network's output at the current epoch, making it a valuable target for training. By implementing this method, we've established new benchmarks in two prominent semi-supervised learning datasets. Specifically, we've reduced the classification error rate from 18.44% to 7.05% in SVHN using 500 labels, and from 18.63% to 16.55% in CIFAR-10 with 4000 labels. These improvements further extend to 5.12% and 12.16% when standard augmentations are applied. Additionally, we've observed a significant boost in CIFAR-100 classification accuracy by incorporating random images from the Tiny Images dataset as extra unlabeled inputs during training. Lastly, our method exhibits robustness against inaccuracies in the labeling process.

Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge players is called Double Dummy Bridge Problem (DDBP). Artificial Neural Networks are Non – Linear mapping structures based on the function of the human brain. Feed Forward Neural Network is used to solve the DDBP in contract bridge. The learning methodology, supervised learning was used in Back – Propagation Network (BPN) for training and testing the bridge sample deal. In our study we compared back – Propagation algorithm and obtained that Resilient Back – Propagation algorithms by using Hyperbolic Tangent function and Resilient Back – Propagation algorithm produced better result than the other. Among various neural network architectures, in this study we used four network architectures viz., 26x4, 52, 104 and 52x4 for solving DDBP in contract bridge.

The problem of learning a general input-output relation using a layered neural network is discussed in a statistical framework. By imposing the consistency condition that the error minimization be equivalent to a likelihood maximization for training the network, the authors arrive at a Gibbs distribution on a canonical ensemble of networks with the same architecture. This statistical description enables them to evaluate the probability of a correct prediction of an independent example, after training the network on a given training set. The prediction probability is highly correlated with the generalization ability of the network, as measured outside the training set. This suggests a general and practical criterion for training layered networks by minimizing prediction errors. The authors demonstrate the utility of this criterion for selecting the optimal architecture in the continuity problem. As a theoretical application of the statistical formalism, they discuss the question of learning curves and estimate the sufficient training size needed for correct generalization, in a simple example.<<ETX>>

As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cogniti ve Psychology, with applications in Distributed Artificial Intell igence (DAI), Autonomous Agents and Robotics. Areas: Robotics, Agent-oriented programming, Neural Nets

The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, because the temporal or spatial information in neighboring ways is disregarded. More parameters are required to learn the complicated data structure. This paper presents a new tensor-factorized NN (TFNN), which tightly integrates TF and NN for multiway feature extraction and classification under a unified discriminative objective. This TFNN is seen as a generalized NN, where the affine transformation in an NN is replaced by the multilinear and multiway factorization for tensor-based NN. The multiway information is preserved through layerwise factorization. Tucker decomposition and nonlinear activation are performed in each hidden layer. The tensor-factorized error backpropagation is developed to train TFNN with the limited parameter size and computation time. This TFNN can be further extended to realize the convolutional TFNN (CTFNN) by looking at small subtensors through the factorized convolution. Experiments on real-world classification tasks demonstrate that TFNN and CTFNN attain substantial improvement when compared with an NN and a convolutional NN, respectively.

In computer vision many tasks are solved using machine learning. In the past few years, state of the art results in computer vision have been achieved using deep learning. Deeper machine learning architectures are better capable in handling complex recognition tasks, compared to previous more shallow models. Many architectures for computer vision make use of convolutional neural networks which were modeled after the visual cortex. Currently deep convolutional neural networks are the state of the art in computer vision. Through a literature survey an overview is given of the components in deep convolutional neural networks. The role and design decisions for each of the components is presented, and the difficulties involved in training deep neural networks are given. To improve deep learning architectures an analysis is given of the activation values in four different architectures using various activation functions. Current state of the art classifiers use dropout, max-pooling as well as the maxout activation function. New components may further improve the architecture by providing a better solution for the diminishing gradient problem.

We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.

We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.

In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs into a set of flat vectors. However, in this way, important topological information may be lost and the achieved results may heavily depend on the preprocessing stage. This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs. A learning algorithm for GNNs is proposed and some experiments are discussed which assess the properties of the model.

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. r 2006 Elsevier B.V. All rights reserved.

We address the problem of serving Deep Neural Networks (DNNs) efficiently from a cluster of GPUs. In order to realize the promise of very low-cost processing made by accelerators such as GPUs, it is essential to run them at sustained high utilization. Doing so requires cluster-scale resource management that performs detailed scheduling of GPUs, reasoning about groups of DNN invocations that need to be co-scheduled, and moving from the conventional whole-DNN execution model to executing fragments of DNNs. Nexus is a fully implemented system that includes these innovations. On large-scale case studies on 16 GPUs, Nexus shows 1.8-41× better throughput than state-of-the-art systems while staying within latency constraints >99% of the time.


Question: Cooperative training methods applied to deep neural network architectures.

This paper delves into the stability of gradient descent optimization in generative adversarial networks (GANs), an area that remains enigmatic despite the increasing importance of GANs. Focusing on the "gradient descent" variant of GAN optimization, where small gradient steps are concurrently applied to both the generator and discriminator parameters, we uncover significant insights. Our analysis reveals that, contrary to common belief, GAN optimization does not always conform to a convex-concave game, even with straightforward parameterizations. Nonetheless, we demonstrate that equilibrium points in this optimization procedure can indeed be locally asymptotically stable under specific conditions for the conventional GAN formulation. Conversely, we highlight the potential for non-convergent limit cycles near equilibrium in the recently introduced Wasserstein GAN. Inspired by our stability investigation, we introduce a novel regularization term for gradient descent GAN updates. This term not only ensures local stability for both the WGAN and the traditional GAN but also exhibits practical benefits by accelerating convergence and mitigating the issue of mode collapse. Our findings underscore the importance of understanding the dynamics of GAN optimization and offer a promising avenue for enhancing GAN training stability and efficiency.

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3× to 4× layerwise speedup and 3× to 7× better energy efficiency.

This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.

Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal perparameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse optimization tasks capturing common properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on. We release an open source implementation of the meta-training algorithm.

As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

Convolutional Neural Networks (CNN) are verycomputation-intensive. Recently, a lot of CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we observed that there is a big mismatch between the parallel types supported by computing engine and the dominant parallel types of CNN workloads. This mismatch seriously degrades resource utilization of existing accelerators. In this paper, we propose aflexible dataflow architecture (FlexFlow) that can leverage the complementary effects among feature map, neuron, and synapse parallelism to mitigate the mismatch. We evaluated our design with six typical practical workloads, it acquires 2-10x performance speedup and 2.5-10x power efficiency improvement compared with three state-of-the-art accelerator architectures. Meanwhile, FlexFlow is highly scalable with growing computing engine scale.

Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domain with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. As a demonstration, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training it for robustness to novel problems. This provides the largest-scale demonstration of heterogeneous DG to date.

Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a “partial” approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

This paper introduces gradient descent methods applied to meta-leaming (leaming how to leam) in Neural Networks. Meta-leaning has been of interest in the machine leaming field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved leaming algorithms. Many previous neural network-based approaches toward meta-leaming have been based on evolutionary methods. We show how to use gradient descent for meta-leaming in recurrent neural networks. Based on previous work on FixedWeight Leaming Neural Networks, we hypothesize that any recurrent network topology and its corresponding leaming algorithm(s) is a potential meta-leaming system. We tested several recurrent neural network topologies and their corresponding forms of Backpropagation for their ability to meta-leam. One of our systems, based on the Long Short-Term Memory neural network developed a leaming algorithm that could leam any two-dimensional quadratic function (from a set of such functions} after only 30 training examples.

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.