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---
license: mit
datasets:
- mjjung/ActivityNet-VTune
language:
- en
---
# TimeChat-7B-ActivityNet-VTune Model
## Model details
We trained [VideoLLaMA](https://arxiv.org/abs/2306.02858) using VTune, a developed instruction-tuning method specifically designed to account for consistency.
For the tuning, we utilized 10K training videos from ActivityNet-Captions with 205K automatically generated annotations.
## Evaluation
We evaluated the model on ActivtyNet-CON and ActivtyNet-Captions.
- ActivityNet-CON
| Metric | Value |
|-----------------|-------------|
| Ground | 33.0 |
| R-Ground | 24.7 (74.8) |
| S-Ground | 10.0 (30.2) |
| H-Verify | 20.2 (61.1) |
| C-Verify | 17.7 (53.7) |
- ActivityNet-Captions
| Metric | Value |
|-----------------|---------|
| R@1 IoU=0.3 | 51.58 |
| R@1 IoU=0.5 | 34.38 |
| R@1 IoU=0.7 | 19.18 |
| mIoU | 36.16 |
**Paper and Code for more information:**
[Paper](https://arxiv.org/abs/2411.12951), [Code](https://github.com/minjoong507/consistency-of-video-llm)
## Citation
If you find our research and codes useful, please consider starring our repository and citing our paper:
```
@article{jung2024consistency,
title={On the Consistency of Video Large Language Models in Temporal Comprehension},
author={Jung, Minjoon and Xiao, Junbin and Zhang, Byoung-Tak and Yao, Angela},
journal={arXiv preprint arXiv:2411.12951},
year={2024}
}
``` |