
\section{List of ToDos}
  \listoftodos
}{}


\end{document}



\section{Ensembling with Standard Prompts}
\label{sec:Ensemblingstandard}

We also consider using LLM generated prompts from CuPL (full prompts) in addition to hand-written prompts. We do this by averaging together all the text embeddings of the CuPL prompts and hand-written prompts. This method yields improvement on all examined datasets compared to the standard method. It outperforms CuPL (full prompts) in 5 of the 8 examined datasets.

\begin{table}[!htbp]
\begin{center}
\begin{tabular}{S|SSSSSSSSS}
    {} & {\rotatebox[origin=l]{90}{ImageNet \cite{deng2009imagenet}}} & {\rotatebox[origin=l]{90}{DTD \cite{cimpoi14describing}}} & {\rotatebox[origin=l]{90}{Stanford Cars \cite{KrauseStarkDengFei-Fei_3DRR2013}}} & {\rotatebox[origin=l]{90}{SUN397 \cite{xiao2010sun}}} & {\rotatebox[origin=l]{90}{Food101 \cite{bossard14}}} & {\rotatebox[origin=l]{90}{FGVC Aircraft \cite{maji13fine-grained}}} & {\rotatebox[origin=l]{90}{Oxford Pets \cite{parkhi2012cats}}} & {\rotatebox[origin=l]{90}{Caltech101 \cite{fei2004learning}}} \\ \midrule
    \text{standard \cite{radford2021learning}}  & {75.54} & {55.20} & {{77.53}} & {69.31} & {93.08} & {32.88} & {93.33} & {93.24} \\\midrule

    \text{CuPL (full prompts)}  & {{76.60}} & {{61.70}} & {77.63} & {{73.31}} & {{93.36}} & {{36.11}} & {{93.81}} & {{93.43}}  \\
    $\Delta$ \text{ standard} & { \footnotesize \color{green!60!black} +1.06} & {\footnotesize \color{green!60!black} +6.50} & {\footnotesize \color{green!60!black} +0.10} & {\footnotesize \color{green!60!black} +4.00} & {\footnotesize \color{green!60!black} +0.28} & {\footnotesize \color{green!60!black} +3.23} & {\footnotesize \color{green!60!black}+0.48} & {\footnotesize \color{green!60!black} +0.19}    \\\midrule
    
    
    \text{CuPL + standard}  & {76.50} & {61.60} & {77.66} & {73.51} & {93.42} & {36.47} & {93.71} & {93.87} \\
     
    $\Delta$ \text{ standard} & {\footnotesize \color{green!60!black} +0.96} & {\footnotesize \color{green!60!black} +6.40} & {\footnotesize \color{green!60!black} +0.13} & {\footnotesize \color{green!60!black} +4.20} & {\footnotesize \color{green!60!black} +0.34} & {\footnotesize \color{green!60!black}+3.59} & {\footnotesize \color{green!60!black}+0.38} & {\footnotesize \color{green!60!black} +0.63}   \\\midrule

\end{tabular}

\caption{Accuracy of CuPL prompts when ensembled with standard prompts}
\label{Tab:ensemble}
\end{center}

\end{table}

\section{CuPL (Full Prompts) vs Standard Prompts}
\label{sec:FullPrompts}
We detail the hand-written prompt templates used for CuPL (full prompts) versus standard CLIP \cite{radford2021learning} prompt templates. For CuPL, hand-written prompt templates are needed for the LLM-prompts, while for the standard method hand-written prompt templates are needed for the image-prompts.

Note that many of the hand-written templates for the standard method encode information about the datasets. For example, "a toy \{\}" demonstrates knowledge that objects are sometimes represented as a toy version of an object rather than as the literal object. CuPL prompts remain much more general (e.g. "Describe what a \{\} looks like").




\vspace{0.5cm}

{\Large
\textbf{Caltech101}}

\begin{table}[!htbp]

\small
\begin{tabular}{p{5.5cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
Describe what a(n) \{\} looks like & a photo of a \{\}.        & a photo of the \{\}.      \\
What does a(n) \{\} look like?     & a painting of a \{\}.     & a painting of the \{\}.   \\
Describe a photo of a(n) \{\}      & a plastic \{\}.           & the plastic \{\}.         \\
                                   & a sculpture of a \{\}.    & a sculpture of the \{\}.  \\
                                   & a sketch of a \{\}.       & a sketch of the \{\}.     \\
                                   & a tattoo of a \{\}.       & a tattoo of the \{\}.     \\
                                   & a toy \{\}.               & the toy \{\}.             \\
                                   & a rendition of a \{\}.    & a rendition of the \{\}.  \\
                                   & a embroidered \{\}.       & the embroidered \{\}.     \\
                                   & a cartoon \{\}.           & the cartoon \{\}.         \\
                                   & a \{\} in a video game.   & the \{\} in a video game. \\
                                   & a plushie \{\}.           & the plushie \{\}.         \\
                                   & a origami \{\}.           & the origami \{\}.         \\
                                   & art of a \{\}.            & art of the \{\}.          \\
                                   & graffiti of a \{\}.       & graffiti of the \{\}.     \\
                                   & a drawing of a \{\}.      & a drawing of the \{\}.    \\
                                   & a doodle of a \{\}.       & a doodle of the \{\}.     \\

\end{tabular}
\end{table}

\vspace{0.5cm}

\textbf{\Large Stanford Cars}

\begin{table}[!htbp]

\begin{tabular}{p{11cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
How can you identify a(n) \{\}?                                       & a photo of a \{\}.        &  \\
Description of a(n) \{\}, a type of car.                              & a photo of the \{\}.      &  \\
A caption of a photo of a(n) \{\}:                  & a photo of my \{\}.       &  \\
What are the primary characteristics of a(n) \{\}?     & i love my \{\}!           &  \\
Description of the exterior of a(n) \{\}                                    & a photo of my dirty \{\}. &  \\
What are the identifying characteristics of a(n) \{\}, a type of car?                             & a photo of my clean \{\}. &  \\
Describe an image from the internet of a(n) \{\} & a photo of my new \{\}.   &  \\
What does a(n) \{\} look like?            & a photo of my old \{\}.   &  \\
Describe what a(n) \{\}, a type of car, looks like              &                              & 

\end{tabular}
\end{table}

\vspace{0.8cm}


\textbf{\Large Food101}

\begin{table}[!htbp]

\begin{tabular}{p{9cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
Describe what \{\} looks like                         & a photo of \{\}, a type of food. &  \\
Visually describe \{\}                                &                                  &  \\
How can you tell that the food in this photo is \{\}? &                                  & 

\end{tabular}
\end{table}

\vspace{0.5cm}

\newpage

\textbf{\Large Oxford Pets}
\begin{table}[!htbp]
\begin{tabular}{p{7cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
Describe what a pet \{\} looks like  &  a photo of a {}, a type of pet.\\
Visually describe a(n) ‘\{\}’, a type of pet.   &  \\
\end{tabular}
\end{table}


\textbf{ \Large ImageNet}

\begin{table}[!htbp]
\scriptsize
\begin{tabular}{l|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
Describe what a(n) \{\} looks like               & a bad photo of a \{\}.               & the origami \{\}.                   \\
How can you identify a(n) \{\}?                  & a photo of many \{\}.                & the \{\} in a video game.           \\
What does a(n) look like?                        & a sculpture of a \{\}.               & a sketch of a \{\}.                 \\
Describe an image from the internet of a(n) \{\} & a photo of the hard to see \{\}.     & a doodle of the \{\}.               \\
A caption of an image of a(n) \{\}:              & a low resolution photo of the \{\}.  & a origami \{\}.                     \\
                                                 & a rendering of a \{\}.               & a low resolution photo of a \{\}.   \\
                                                 & graffiti of a \{\}.                  & the toy \{\}.                       \\
                                                 & a bad photo of the \{\}.             & a rendition of the \{\}.            \\
                                                 & a cropped photo of the \{\}.         & a photo of the clean \{\}.          \\
                                                 & a tattoo of a \{\}.                  & a photo of a large \{\}.            \\
                                                 & the embroidered \{\}.                & a rendition of a \{\}.              \\
                                                 & a photo of a hard to see \{\}.       & a photo of a nice \{\}.             \\
                                                 & a bright photo of a \{\}.            & a photo of a weird \{\}.            \\
                                                 & a photo of a clean \{\}.             & a blurry photo of a \{\}.           \\
                                                 & a photo of a dirty \{\}.             & a cartoon \{\}.                     \\
                                                 & a dark photo of the \{\}.            & art of a \{\}.                      \\
                                                 & a drawing of a \{\}.                 & a sketch of the \{\}.               \\
                                                 & a photo of my \{\}.                  & a embroidered \{\}.                 \\
                                                 & the plastic \{\}.                    & a pixelated photo of a \{\}.        \\
                                                 & a photo of the cool \{\}.            & itap of the \{\}.                   \\
                                                 & a close-up photo of a \{\}.          & a jpeg corrupted photo of the \{\}. \\
                                                 & a black and white photo of the \{\}. & a good photo of a \{\}.             \\
                                                 & a painting of the \{\}.              & a plushie \{\}.                     \\
                                                 & a painting of a \{\}.                & a photo of the nice \{\}.           \\
                                                 & a pixelated photo of the \{\}.       & a photo of the small \{\}.          \\
                                                 & a sculpture of the \{\}.             & a photo of the weird \{\}.          \\
                                                 & a bright photo of the \{\}.          & the cartoon \{\}.                   \\
                                                 & a cropped photo of a \{\}.           & art of the \{\}.                    \\
                                                 & a plastic \{\}.                      & a drawing of the \{\}.              \\
                                                 & a photo of the dirty \{\}.           & a photo of the large \{\}.          \\
                                                 & a jpeg corrupted photo of a \{\}.    & a black and white photo of a \{\}.  \\
                                                 & a blurry photo of the \{\}.          & the plushie \{\}.                   \\
                                                 & a photo of the \{\}.                 & a dark photo of a \{\}.             \\
                                                 & a good photo of the \{\}.            & itap of a \{\}.                     \\
                                                 & a rendering of the \{\}.             & graffiti of the \{\}.               \\
                                                 & a \{\} in a video game.              & a toy \{\}.                         \\
                                                 & a photo of one \{\}.                 & itap of my \{\}.                    \\
                                                 & a doodle of a \{\}.                  & a photo of a cool \{\}.             \\
                                                 & a close-up photo of the \{\}.        & a photo of a small \{\}.            \\
                                                 & a photo of a \{\}                     & a tattoo of the \{\}.               \\

\end{tabular}
\end{table}


\textbf{\Large FGVC Aircraft}

\begin{table}[!htbp]

\begin{tabular}{p{4.5cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
Describe a(n) \{\} aircraft  & a photo of a \{\}, a type of aircraft.  &  \\
Describe the \{\} aircraft & a photo of the \{\}, a type of aircraft.   &  \\

\end{tabular}
\end{table}




\vspace{1cm}
\textbf{\Large DTD}

\begin{table}[!htbp]

\begin{tabular}{p{6.5cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
What does “\{\}” material look like?  & a photo of a \{\} texture.   &  \\
What does a “\{\}” surface look like? & a photo of a \{\} pattern.   &  \\
What does a “\{\}” texture look like? & a photo of a \{\} thing.     &  \\
What does a “\{\}” object look like?  & a photo of a \{\} object.    &  \\
What does a “\{\}” thing look like?   & a photo of the \{\} texture. &  \\
What does a “\{\}” pattern look like? & a photo of the \{\} pattern. &  \\
                                      & a photo of the \{\} thing.   &  \\
                                      & a photo of the \{\} object.  & 

\end{tabular}
\end{table}



\vspace{0.5cm}

\textbf{\Large SUN397}

\begin{table}[!htbp]

\begin{tabular}{p{5.5cm}|ll}
    \textbf{CuPL hand-written}                                           & \textbf{Standard hand-written}                             \\ \midrule
Describe what a(n) \{\} looks like & a photo of a \{\}.   &  \\
How can you identify a(n) \{\}?    & a photo of the \{\}. &  \\
Describe a photo of a(n) \{\}      &                      & 

\end{tabular}
\end{table}

\vspace{0.5cm}




\section{CuPL Single Prompts}
\label{sec:SinglePrompts}
The general sentence used in the single prompt setting is: \begin{center} "Describe what a(n) \{\}\textbf{, a type of \_,} looks like" \end{center} Here we specify the type filled in for each of the examined datasets:

\begin{table}[!htbp]

\begin{tabular}{p{3cm}|ll}
    \textbf{Dataset} & \textbf{Single LLM-prompt template}\\ \midrule
    ImageNet & Describe what a(n) \{\} looks like \\
    DTD & Describe what a(n) \{\}, a type of texture, looks like \\
    StanfordCars & Describe what a(n) \{\}, a type of car, looks like \\
    SUN397 & Describe what a(n) \{\} looks like \\
    Food 101 & Describe what a(n) \{\}, a type of food, looks like \\
    FGVC Aircraft & Describe what a(n) \{\}, a type of aircraft, looks like \\
    Oxford Pets & Describe what a(n) \{\}, a type of pet, looks like \\
    Caltech101 & Describe what a(n) \{\} looks like 


\end{tabular}
\end{table}

\newpage


\section{Evaluation Metric}
\label{sec:metrics}

\normalsize
\begin{table}[!htbp]
\begin{tabular}{SSSSSSSS}
    {\rotatebox[origin=l]{70}{ImageNet }} & {\rotatebox[origin=l]{70}{DTD}} & {\rotatebox[origin=l]{70}{StanfordCars}} & {\rotatebox[origin=l]{70}{SUN397}} & {\rotatebox[origin=l]{70}{Food 101}} & {\rotatebox[origin=l]{70}{FGVC Aircraft}} & {\rotatebox[origin=l]{70}{Oxford Pets}} & {\rotatebox[origin=l]{70}{Caltech101}} \\ \midrule
     \multicolumn{1}{l|}{Acc.} & \multicolumn{1}{l|}{Acc.} & \multicolumn{1}{l|}{Acc.} & \multicolumn{1}{l|}{Acc.} & \multicolumn{1}{l|}{Acc.} & \multicolumn{1}{l|}{Mean} & \multicolumn{1}{l|}{Mean} & {Mean} \\
     \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{} & \multicolumn{1}{l|}{per Class} & \multicolumn{1}{l|}{per Class} & {per Class} \\


\end{tabular}
\end{table}


\section{Example Generated image-prompts}
\label{sec:imageprompts}

\normalsize
A selection of LLM-generated image-prompts for a subset of ImageNet categories. We give all 50 image-prompts for the first ImageNet category of "Tench" and then 10 randomly selected prompts for a number of randomly selected ImageNet categories. 

\subsection{All generated image-prompts for "Tench" category}
{ \small
\begin{spverbatim}
"A tench is a freshwater fish of the carp family.", 
"A tench is a freshwater fish that is typically brown or olive in color.", 
"A tench is a fresh water fish that can grow up to 2 feet in length.", 
"A tench is a freshwater fish of the family Cyprinidae.", 
"A tench is a freshwater fish of the carp family.", 
"A tench is a freshwater fish with a dark green back and light-colored sides.", 
"Tench are a freshwater fish found in Europe.", 
"A tench is a small freshwater fish in the carp family.", 
"A tench is a heavyset freshwater fish with a mottled brown body and a small, flat head.", 
"A tench is a freshwater fish that looks similar to a carp.", 
"A tench is a freshwater fish in the carp family.", 
"A tench is a freshwater fish of the Cyprinidae family.", 
"The tench is a freshwater fish of the Cyprinidae family.", 
"The tench is a fresh-water fish in the family Cyprinidae.", 
"The easiest way to identify a tench is by its herringbone-patterned scales.", 
"A tench is a freshwater fish of the carp family.", 
"Tench are a freshwater fish found in Europe.", 
"Tench have a large, slimy body with scales that have a green hue.", 
"The tench is a freshwater fish belonging to the carp family.", 
"A tench is a freshwater fish of the Cynoglossidae family.", 
"A tench is a freshwater fish in the carp family.", 
"Tensch are freshwater fish with Olive Green backs, shading to Yellowish on the sides.", 
"A tench looks like a green freshwater fish with a brownish hue.", 
"A tench looks like a freshwater fish with a dark olive-green back, fading to yellowish-brown on the sides.", 
"A tench usually has olive-green skin with dark spots, and a orange-yellow underbelly.", 
"Tench are a freshwater fish that can grow up to 70cm long! They have olive-brown skin with dark spots, and their meat is white and firm.", 
"A tench is a freshwater fish with a sturdy body and a greenish-brown coloration.", 
"A tench is a freshwater fish that can grow up to about two feet long.", 
"A tench is a freshwater fish in the carp family.", 
"A tench is a large, freshwater fish with a thick body and large head.", 
"The image is of a tench fish swimming in water.", 
"The image is of a tench fish swimming in a pond.", 
"The tench is a freshwater fish native to Europe.", 
"This image shows a large, dark green tench swimming in a pond.", 
"An image of a tench from the internet would likely show a dark green fish with a lighter underside.", 
"The image is of a tench fish.", 
"The image is of a tench fish on a white background.", 
"A tench is a freshwater fish of the Cyprinidae family.", 
"The image is of a tench swimming in a murky pond.", 
"In the image, a tench swims in a pond with lily pads.", 
" A tench in a river.", 
"A tench (Tinca tinca) is a freshwater fish in the carp family that is found throughout Europe.", 
" Tench (Tinca tinca), a member of the carp family (Cyprinidae), native to Eurasia.", 
" A tench, a freshwater fish in the family Cyprinidae.", 
" The tench (Tinca tinca) is a freshwater fish of the cyprinid family found throughout Eurasia.", 
" A tench in a Finnish lake.", 
"A tench (Tinca tinca) is a freshwater fish belonging to the carp family (Cyprinidae).", 
"A tench in a fishpond.", 
" The common tench is a freshwater fish of the cyprinid family found throughout Eurasia.", 
"Tench (Tinca tinca) in a pond."
    
\end{spverbatim}
}

\subsection{Sample generated image-prompts for randomly selected ImageNet categories}

{ \small
\begin{spverbatim}
"bubble":
"A bubble is a sustained period of inflated asset prices.", 
"A bubble looks like a sphere of air.", 
"A bubble is often characterized by rapidly increasing prices in an asset or security, followed by a sharp decrease in prices.", 
"A bubble looks like a small, round, thin film of soap filled with air.", 
"A bubble looks like a round sphere of soap film.", 
" \"A bubble being blown in the park.", 
"A close-up of a soap bubble with a thin film of water in between two layers of air.", 
"A bubble is a spherical shape made up of a thin film of soap water.", 
"A bubble is a circle of air surrounded by water.", 
"A bubble looks like a round, thin layer of soap surrounding a pocket of air."

"kit fox":
"A kit fox is a small species of fox, about the size of a domestic cat.", 
"You can identify a kit fox by its small size, its big ears, and its long, bushy tail.", 
" A kit fox laying on the ground in a desert habitat.", 
"A kit fox is a small fox found in North America.", 
"A kit fox is a small fox with a sleek coat of fur.", 
"A kit fox is a small fox with large ears, a long, black-tipped tail, and pale fur.", 
"A kit fox has a reddish coat, with white patches on its chest and throat.", 
"This kit fox has a reddish coat and large ears.", 
"A kit fox looks like a small fox with a pointed nose, large ears, and a long, bushy tail.", 
"A kit fox is a small species of fox."

"toy terrier":
"A toy terrier looks like a very small version of a terrier.", 
"The image is of a toy terrier that is mostly white with brown spots.", 
"You can identify a toy terrier by looking for a compact, short-legged dog with a short muzzle.", 
" Cute little guy.", 
"Toy terriers are miniature versions of terriers, such as the Jack Russell Terrier.", 
"A toy terrier is a small, lightweight breed of dog.", 
"The image is of a small, brown toy terrier.", 
"A toy terrier typically has a long, narrow head with pointy ears, and a small, compact body.", 
"A toy terrier is a small, short-legged dog with a long body, pointy nose, and large ears.", 
"A small, brown and white toy terrier is sitting on a beige couch, looking at the camera."

"mousetrap":
"In the image, there is a mousetrap made of wood and metal.", 
"An image of a mousetrap from the internet would most likely show a traditional wooden mousetrap with a metal spring.", 
"A mousetrap is a device made to catch and kill mice.", 
"A mousetrap is a small device that is used to catch mice.", 
"The classic mousetrap consists of a wooden base with a metal spring mounted on one end.", 
"The mousetrap is a small wooden box with a metal spring inside.", 
"The mousetrap is a simple device that has been used for centuries to catch mice.", 
"The classic mousetrap - simple, effective, and deadly.", 
"The most common way to identify a mousetrap is by its small size and rectangular shape.", 
"A mousetrap typically has a wire or wooden frame that is baited with food and springs open quickly to snap shut on the mouse when it attempts to steal the bait."

"dog sled":
"A dog sled is a vehicle on runners, typically with a thin frame and a flat bottom, that is used to convey goods or passengers over snow or ice.", 
"A dog sled is a toboggan pulled by dogs, typically over snow.", 
"A dog sled looks like a bed on runners that is pulled by dogs.", 
"A dog sled is typically a heavy frame on runners that is pulled by one or more dogs.", 
"A dog sled is traditionally a sled pulled by dogs, used for transportation, racing, or other purposes.", 
" teamA dog sled team is a group of dogs that are harnessed together to pull a sled.", 
"A dog sled looks like a small, open vehicle that is pulled by one or more dogs.", 
"Dog sledding in winter.", 
" pulled by huskiesThe image is of a dog sled pulled by huskies.", 
"A dog sled looks like a large cart that is pulled by a team of dogs."

"geyser":
"A geyser is a hot spring that periodically erupts, shooting a column of water and steam into the air.", 
"A geyser looks like a hole in the ground that sometimes spurts hot water and steam into the air.", 
"Geysers are hot springs that periodically spout water and steam into the air.", 
"The image is of a geyser erupting.", 
"A geyser is a hot spring that periodically erupts, spraying water into the air.", 
"A geyser typically looks like a cone of rocks with a small hole at the top.", 
"A geyser is a hot spring where water intermittently boils, sending a jet of hot water and steam into the air.", 
"A geyser is a hot spring that periodically shoots a stream of hot water and steam into the air.", 
"The image is of a geyser shooting water high into the air.", 
"A geyser looks like a column of water that shoots into the air and then falls back down."

"Schipperke":
"A Schipperke is a small, Belgian breed of dog.", 
"A Schipperke is a small black Belgian dog with a rat-like tail.", 
"The image is of a black and white dog with pointy ears and a long body.", 
"A Schipperke is a small, black, Belgian breed of dog.", 
"A Schipperke is a small, black, Belgian breed of dog that closely resembles a fox.", 
"A Schipperke is a small Belgian breed of dog that resembles a fox.", 
"Schipperkes have a long, black coat and a pointed muzzle.", 
"A Schipperke is a small dog breed with a fox-like appearance.", 
"It's a photo of a black and tan Schipperke dog standing in front of a brick wall.", 
"Black, small, spitz-type dog with a long, fox-like snout, large erect ears, and a long, high-set tail."

"go-kart":
"A go-kart typically looks like a small car or buggy with a small engine in the back.", 
"A go-kart is a small vehicle with four wheels, a steering wheel, and a gas pedal.", 
"A go-kart is a small vehicle with a steering wheel, pedals, and an engine.", 
"Two young girls in go-karts race down a path in a park.", 
"A go-kart is a small, lightweight vehicle with four wheels and a simple, open frame.", 
"A go-kart is a small, open-wheeled vehicle used for racing.", 
"Two kids racing go-karts on a dirt track.", 
"A go-kart is a small, racing car.", 
"A go-kart typically looks like a small, open-wheeled car.", 
"A go-kart is a small, lightweight vehicle with four wheels that is propelled by a small engine."

"black-and-white colobus":
"The Black-and-White Colobus is a type of Old World monkey, found in Africa.", 
"A black-and-white colobus has long black fur, and a white face with a black triangle around the eyes.", 
"Colobus Monkey in the TreesThis elegant colobus monkey is swinging through the trees in search of food.", 
" monkeyIn this image, a black-and-white colobus monkey is shown perched atop a tree branch.", 
"The black-and-white colobus monkey is one of the most beautiful and distinctive of all the colobus monkeys.", 
"The black-and-white colobus monkey is a species of primate in the Colobidae family.", 
"Colobus monkeys are generally black with white patches on their face, back, and sides.", 
"The black-and-white colobus is a species of Old World monkey.", 
" monkeyThe image is of a black-and-white colobus monkey sitting on a tree branch.", 
" monkeyIn the image, the black-and-white colobus monkey is sitting in a tree."

"sock":
"A sock usually has a cuff at the top, and a heel at the bottom.", 
" A black sock with a white line running down the middle.", 
"A sock is typically a garment worn on the feet and made from a soft material, such as cotton.", 
"A sock normally has a heel, toe and a cuff at the top.", 
"A sock is a small amount of money that is given to someone without them knowing.", 
"A sock is an article of clothing worn on the feet.", 
"A sock is a piece of clothing that is worn on the feet.", 
"A sock is a tubular garment that covers the foot and ankle.", 
"This image is of a blue and white striped sock.", 
"There are many ways that you can identify a sock."

"Cocker Spaniel":
"A Cocker Spaniel is a medium sized dog with long, floppy ears, and a silky coat that is usually either brown or black.", 
"The Cocker Spaniel has a long floppy ears, a silky coat, and a bushy tail.", 
"A Cocker Spaniel has a long, black muzzle and big, brown eyes.", 
"The Cocker Spaniel is a breed of dog.", 
"An image of a Cocker Spaniel from the internet shows a small brown and white dog with long floppy ears.", 
"The image is of a Cocker Spaniel with short, brown fur and long, floppy ears.", 
"Cocker spaniels have long, floppy ears and a long, silky coat.", 
"A Cocker Spaniel is a small to medium sized dog.", 
"The image is of a light brown and white Cocker Spaniel standing on a green grassy field with its head turned to the side.", 
"A Cocker Spaniel has a long, silky coat that is usually either black, brown, or golden."

"southern black widow":
"A southern black widow spider perched atop a web.", 
"Female southern black widows have a black body with a red hourglass shape on their abdomen.", 
"The southern black widow is black with a red hourglass shape on its belly.", 
"Female southern black widow spiders are black with a characteristic red hourglass-shaped mark on their ventral abdomen.", 
" spiderThe image is of a large, black spider with a red hourglass shape on its abdomen.", 
"A southern black widow is a spider that is black with a red hourglass shape on its abdomen.", 
"A southern black widow spider is a small, black spider with a red hourglass-shaped mark on its underside.", 
"There's an image on the internet of a southern black widow that's really cool.", 
"A southern black widow can be identified by its black coloration with a red hourglass shape on its abdomen.", 
"A Southern black widow is a type of spider that is black with a red hourglass shape on its belly."

"catamaran":
"A catamaran is a sailboat that has two hulls, or wide bodies, that are connected by beams.", 
"The easiest way to identify a catamaran is by its two hulls.", 
"A catamaran is a type of boat that has two parallel hulls.", 
"A catamaran is a multi-hulled vessel with two parallel hulls of equal size.", 
"My dream boat! A sleek catamaran that can zip through the waves.", 
"A catamaran is a multi-hulled vessel with two parallel hulls of equal size.", 
"The image is of a white catamaran with blue trim.", 
"A catamaran is a type of boat that has two hulls, or platforms, that are parallel to each other.", 
"The image is of a yellow catamaran with white trim, sitting in calm water.", 
"A catamaran is a type of sailing vessel that consists of two parallel hulls of equal size."

"beach":
".", 
"Blue skies, white sands, and clear turquoise waters make this beach a paradise.", 
"A beach usually has sand and water.", 
"Beach identification can be accomplished through the identification of physical characteristics.", 
"A beach is a naturally occurring feature of the landscape.", 
"The sun sets over the ocean, casting a beautiful orange hue in the sky.", 
"A beach is a large body of water with sand or small rocks at the shore.", 
"In the image, the beach is Brilliant white with crystal blue waters.", 
"The beach looks like a long strip of land next to the ocean.", 
"A beach typically looks like a large, flat expanse of sand with some rocks or other natural features nearby."

"rotary dial telephone":
"A rotary dial is a device used to dial telephone numbers.", 
"Rotary dial telephone from the mid-20th century.", 
"A rotary dial telephone is a phone with a circular dial on the front face.", 
"When looking at a rotary telephone, you can tell it is a rotary phone by the Place the phone's receiver on your ear and listen for a dial tone.", 
"A rotary dial phone is an older model phone that has a circular device with numbers on it that you rotate with your finger to dial a number.", 
"A rotary dial telephone is a type of telephone that uses a mechanical dial to select the telephone number that a user wishes to call.", 
"History of the Rotary Dial Telephone.", 
"A rotary dial telephone is an old-fashioned telephone that has a round dial on the front of it.", 
" rotary dial telephone looks like a classic telephone with a rotary dial on the base unit.", 
"The image is of a rotary dial telephone on a black background."


\end{spverbatim}
}




\section{Conclusion}
We demonstrate that leveraging knowledge from an LLM can immediately improve zero-shot accuracy on a variety of image classification tasks, with much less hand-engineering efforts to craft natural language prompts. Furthermore, prompts can be customized to the desired categories, rather than a general template that applies to all existing image categories. Finally, using prompts generated by LLMs lowers the barrier of prior knowledge about the dataset, which is often required when crafting prompt templates. 

Querying an LLM for prompt construction is simple, straightforward and as our results suggested, immediately beneficial. The hypothesis that a joint force of LLMs and open vocabulary models would improve zero-shot image classification is thoroughly tested in this work. 
We hope these findings serve as a useful tool towards understanding and improving zero-shot image classification, and more generally, the consolidation of model capacities and modalities through natural language. \\
\section{Experiments and Results}
We first discuss the details of our experimental setup. We next show improvements on a wide range of image classification benchmarks. We then examine the scaling behavior with respect to the model size and report observations regarding hyperparameters such as the LLM sampling temperature. Finally, we consider and compare with other methods of obtaining descriptive captions. 

\begin{figure*}
    \centering
  \includegraphics[scale=.29]{figures/scale.pdf}
   \caption{\textbf{Performance of CuPL as models scale.} (Left) ImageNet Top-1 accuracy for various scales of CLIP. CuPL remains consistently better than baseline even we adjust CLIP model size (ViT-B/32, ViT-B/16, ViT-L/14). GPT-3 model set as DaVinci-002. (Right) ImageNet Top-1 accuracy for various scales of GPT-3 (ada, babbage, curie, davinci-002). Larger models produce higher accuracy. CLIP model set as ViT-L/14.}
   \label{fig:scale}
\end{figure*}


\subsection{Setup}
Unless specified otherwise, we use CLIP with a backbone of ViT-L/14 \cite{dosovitskiy2020image} and the GPT-3 DaVinci-002 model. Additionally, in order to perform open vocabulary image classification, each image category needs a natural language label. This is sometimes provided by the dataset, but not always (e.g. ImageNet categories are described by an id number which can map to multiple synonyms). For this work, we use the same natural language labels specified in Radford \emph{et al. } \cite{radford2021learning}. 



\subsection{Results}
Our results for the single prompt setting and the full prompts setting are in Table \ref{Tab:results}.  We present our method's performance on 8 different image classification benchmarks, comparing both the classification accuracy and the number of hand-written sentence templates needed for each method. Note that for the standard method \cite{radford2021learning}, the hand-written sentences refer to the image-prompts, while for CuPL the hand-written sentences refer to the LLM-prompts, with which image-prompts are generated.

\textbf{1. CuPL (single prompt).} In this setting, we see performance gains in 7 out of the 8 examined datasets. Note this setting uses \emph{a single hand-constructed sentence across all datasets}. This is in comparison to the nearly 100 unique image-prompt templates that are hand-written across all of these datasets in the standard setting. Additionally, in the standard setting these hand-constructed prompts must be very specific to the dataset (e.g., "a black and white photo of a \{\}.", "a plastic \{\}."). In comparison, CuPL (single prompt) requires one single word of customization across different datasets (the category type of the overall dataset) and still outperforms the hand-written, domain specified baseline in almost all cases. Thus, we present this single prompt setting as a simple baseline that matches or exceeds prompt engineering open vocabulary models. 

\textbf{2. CuPL (full prompts).} Here we see improvements on all examined datasets. This includes large (over 1 percentage point) gains on ImageNet Top-1, DTD (texture classification), SUN397 (scene classification), and FGVC Aircraft (fine-grained aircraft classification). While this setting requires more hand-written prompts than setting (1), it still requires significantly fewer than the baseline method (5 sentences versus 80 sentence for ImageNet), and does not include knowledge about the image domain. The full list of hand-constructed sentences for CuPL (full prompts) and the baseline method \cite{radford2021learning} can be found in Section \ref{sec:FullPrompts} of the Appendix. 




\begin{figure*}

\centering
  \includegraphics[scale=.29]{figures/hyperparams.pdf}
   \caption{\textbf{Ablation on number of LLM-prompts (left) and image-prompts (right).} (Left) As number of hand-written LLM-prompts increases, so does accuracy. 10 image-prompts are generated for each LLM-prompt. Note that CuPL outperforms the baseline even with a single hand-written sentence.  (Right) We adjust the number of image-prompts generated by a fixed number (5) of LLM-prompts. Even with 25 image-prompts (5 per LLM-prompt), we outperform the baseline which uses 80 image-prompts.}
   \label{fig:hyperparams}
\end{figure*}



\subsection{Analysis and Ablations}

\textbf{Model Size.} In Figure \ref{fig:scale}, we show CuPL (full prompts) at different model scales. As there are two different zero-shot models in the CuPL algorithm, we show the effects of varying each model individually. On the left hand side, we vary the CLIP model used while holding the LLM constant. We see consistent gains across all model sizes. On the right hand side, we vary the size of the LLM. We plot the accuracy of the baseline as well, which does not vary as it does not utilize an LLM. We find larger models lead to higher accuracy, though the 2nd and 3rd largest models perform similarly. 


\begin{wrapfigure}{r}{0.5\textwidth}
    \centering

    {\includegraphics[width=0.45\textwidth]{figures/temp.pdf}}
  \caption{\textbf{Effect of LLM temperature.} More prompt diversity leads to higher performance.}
  \label{fig:temp}
\end{wrapfigure}


\textbf{Number of Prompts.} In Figure \ref{fig:hyperparams}, we present ablations on the number of LLM-prompts and image-prompts for CuPL (full prompts). On the left side, we show ImageNet accuracy as we increase the number of LLM-prompts. This also corresponds to the number of sentences that have to be hand-written. Notably, this methods outperforms the baseline even when using prompts generated from a single hand-written sentence. On the right hand side, we hold the number of LLM-prompts constant at 5 and adjust how many image-prompts we generate per LLM-prompt. We plot the accuracy given the total number of image-prompts (so 10 generated image-prompt per LLM-prompt corresponds to 50 total image-prompts). We see that CuPL begins to outperform the baseline at just 25 image-prompts, well below the 80 image-prompts used in the baseline. 






\textbf{Diversity of Prompts.} We also examine the impact of the diversity of image-prompts on ImageNet accuracy. We adjust this parameter by changing the temperature of the GPT-3 model. This value changes the likelihood of selecting lower probability tokens and makes sentences more diverse from each other. As demonstrated in Figure \ref{fig:temp}, more diverse prompts lead to higher ImageNet accuracy. Note these comparisons are done with a single LLM-prompt to save computational cost.



\begin{wraptable}{r}{0.43\textwidth}
    \centering
    
\begin{tabular}{c|c|c}
    \textbf{Standard} & \textbf{CuPL} &  \textbf{WordNet} \\ \midrule
    75.54 & 76.60 & 73.44
\end{tabular}
  \caption{ImageNet Top-1 accuracy for different methods of generating image-prompts.}
  \vspace{-0.2 cm}
  \label{fig:defs}
\end{wraptable}

\textbf{Generated Prompts versus Category Definitions.} We also consider additional methods to obtain descriptive sentences for each ImageNet category, other than using an LLM. We compare CuPL (full) image-prompts with image-prompts generated using definitions of each ImageNet category. Because each ImageNet category is derived from the WordNet database \cite{miller1995wordnet}, we can use the WordNet definition of each word. We preprocess these definitions so they are of the form "A(n) \{\} is a ..." as not all WordNet definitions contain the name of the word itself. We also add a period to the end of each definition, as we find this increases performance. As shown in Table~\ref{fig:defs}, ImageNet Top-1 accuracy with WordNet definition prompts is below that of CuPL or standard prompts.  In addition to lower accuracy, this method uses significantly more hand-constructed sentences as it requires 1000 unique hand-written definitions compared to 97 unique hand-written image-prompt templates for the standard method and 25 unique hand-written LLM-prompt templates for CuPL (full).
\section{Introduction}
Open vocabulary models \cite{pham2021combined, jia2021scaling, radford2021learning, yu2022coca} achieve high classification accuracy across a large number of datasets without labeled training data for those tasks. To accomplish this, these models leverage the massive amounts of image-text pairs available on the internet by learning to associate the images with their correct caption, leading to greater flexibility during inference. Unlike standard models, these models classify images by providing a similarity score between an image and a caption. To perform inference, one can generate a caption or "prompt" associated with each of the desired categories, and match each image to the best prompt. This means that categories can be selected ad hoc and adjusted without additional training. 

However, this new paradigm poses a challenge:
\begin{center} \emph{How can we best represent an image category through natural language prompts?}\end{center} The standard approach is to hand write a number of prompts templates \cite{radford2021learning} (e.g., "a photo of a \{\}"), compile a natural language label for each category in the dataset, and create a set of prompts for each category by filling in each of these templates with the natural language labels. Then, image embeddings are matched to the nearest set of prompt embeddings and labelled with the category associated with that set of prompts (more details in Section \ref{sec:methods}). 

This method has three major drawbacks. Firstly, each prompt template has to be hand-written, so having twice as many prompts for a category requires twice as much human effort. This can become costly as each new dataset typically has a different set of prompt templates \cite{radford2021learning}. Secondly, the prompt templates must be general enough to apply to all image categories. For example, a prompt for the ImageNet \cite{deng2009imagenet} category "platypus" could only be as specific as "a photo of a \{platypus\}", and could not be something like "a photo of a \{platypus\}, a type of aquatic mammal" as that template would no longer be relevant for other image categories. Lastly, writing high performing prompt templates currently requires prior information about the contents of the dataset. For example, the list of hand-written ImageNet prompts \cite{radford2021learning} includes "a black and white photo of the \{\}.", "a low resolution photo of a \{\}.", and "a toy \{\}." all of which demonstrate prior knowledge about the type of representations present in the dataset. This information is not generalizable to other datasets, as ImageNet contains "black and white" and "toy" representations of its categories, but other datasets do not (e.g., FVGC Aircraft \cite{maji13fine-grained}). 

To overcome these challenges, we propose Customized Prompts via Language models (CuPL). In this algorithm, we couple a large language model (LLM) with a zero-shot open vocabulary image classification model. We use the LLM to generate prompts for each of the image categories in a dataset. Using an LLM allows us to generate an arbitrary number of prompts with a fixed number of hand-written sentences. Additionally, these prompts are now customized to each category and can contain rich visual descriptions while still remaining zero-shot (e.g., "A platypus looks like a beaver with a duck's bill" -- a sentence generated by an LLM). 

We find these customized prompts outperform the hand-written templates on 8 zero-shot image classification benchmarks, including a greater than 1 percentage point gain on ImageNet \cite{deng2009imagenet} Top-1 accuracy and a greater than 6 percentage point gain on Describable Textures Dataset \cite{cimpoi14describing}, with fewer hand-written prompts when compared to the standard method used in Radford \emph{et al. } \cite{radford2021learning}. Finally, this method requires no additional training or labeled data for either model. 

\section{Methods}
\label{sec:methods}

\begin{figure*}[t]
  \centering
  \includegraphics[scale=.27]{figures/qual.pdf}
   \caption{\textbf{Example CuPL LLM-prompts and Image-prompts.} LLM-prompts are filled in with a class name and then used as input to GPT-3, which then outputs image-prompts. Example LLM generated image-prompts and associated images from ImageNet are shown. Only image-prompts are used for the downstream image classification.}
   \label{fig:qual}
\end{figure*}

The CuPL algorithm consists of two steps: (1) generating customized prompts for each of the categories in a given dataset and (2) using these prompts to perform zero-shot image classification.

\subsection{Generating Customized Prompts}

This step consists of generating prompts using an LLM. For clarity, we distinguish between two different kind of prompts. The first are the prompts which cue the LLM to generate the descriptions of the dataset categories. These prompts do not describe an object, but rather prompt the description of an object (e.g., "What does a platypus look like?"). We will refer to these as "LLM-prompts".


Secondly there are the prompts to be matched with images in the zero-shot image classification model. These are the prompts that describe a category (e.g., "A platypus looks like ..."). We call them "image-prompts." These are the output of the LLM, as examplified in Figure~\ref{fig:qual}.


 In this work, we use GPT-3 \cite{brown2020language} as our LLM. To generate our image-prompts, we must first construct a number of LLM-prompt templates. While this does require some engineering by hand, it is significantly less than the amount of hand-engineered sentences used in the standard method of creating image-prompt templates for CLIP. For example, in our ImageNet experiments, we construct 5 LLM-prompt templates compared to the 80 image-prompts used by CLIP for zero-shot ImageNet classification.

 After constructing these LLM-prompts, we generate 10 different image-prompts for each of the LLM-prompts. This means for ImageNet we use an LLM to generate a total of 50 customized image-prompts for each image category. For each of these, we generate a maximum of 50 tokens, but halt a generation early if it produces a period. Additionally, we generate with a high temperature of 0.99, which encourages more diversity among the 10 generated image-prompts. We also clean each generated sentences by deleting any blank lines and adding a period at the end. 

\subsection{Utilizing Customized Prompts}

After generating image-prompts for each of the categories, we then perform zero-shot image classification. While there are a number of open vocabulary models \cite{pham2021combined, jia2021scaling, radford2021learning, yu2022coca}, we report our results using CLIP \cite{radford2021learning} as this is the most popular publicly available open vocabulary model. 

CLIP consists of a text encoder and and image encoder (schematic on the left side of Figure \ref{fig:teaser}). In the standard setting, there are a number of hand-written templates which can be completed with the relevant category names (e.g. "A photo of a \{\}", "A photo of many \{\}"). To classify the images in a dataset, each of these templates is filled in with a given category name. Then each of these sentences is embedded via the text encoder, and all sentences completed with the same category name are averaged and normalized. This results in $n$ embeddings where $n$ is the number of categories in the dataset. Each of these $n$ embeddings is the mean of many different sentence embeddings. Then each image in the dataset is embedded using the image encoder. This embedding is compared to each of the $n$ text embeddings using cosine similarity and is labeled with the most similar one.

CuPL requires only a small adjustment from this standard practice. Instead of filling in the hand-written templates for each category, we simply replace these altogether with the sentences output by GPT-3. This means for CuPL, hand-written templates are only used as input for the LLM, while the prompts for CLIP are entirely generated text. We present 2 different setting of CuPL (as shown in Table \ref{Tab:results}), each representing a different trade-off between accuracy and hand-engineering. 


\textbf{1. CuPL (single prompt)}. This setting uses one single hand-written sentence for all datasets. We do this by constructing a general LLM-prompt template which is filled in with the category names for each dataset. This general template is as follows: \begin{center} "Describe what a(n) \{\}\textbf{, a type of \_,} looks like" \end{center} The bolded portion of this template is either filled in with the category type of the dataset ("pet" for Oxford Pets \cite{parkhi2012cats}, "aircraft" for FGVC Aircraft \cite{maji13fine-grained}) or removed for more general datasets, like ImageNet \cite{deng2009imagenet}. Type specification is necessary because of words that have multiple meanings. For example "boxer" from the Oxford Pets dataset can also mean a person who boxes, as opposed to a dog breed, so it is necessary to specify "a type of pet". Similarly, "Tornado" from the FGVC Aircraft dataset can be a type of aircraft or a type of weather. 

\textbf{2. CuPL (full prompts)}. In this setting we use different LLM-prompt templates for each dataset, just as Radford \emph{et al. } \cite{radford2021learning} uses different image-prompt templates for each dataset. However, we use fewer hand-written templates overall and also contain less specific information about each dataset in the templates. For this work, each dataset has between 2 and 9 LLM-prompts which generate between 20 and 90 image-prompt per category (10 generated sentences per LLM-prompt). For ImageNet, we use the following 5 LLM-prompts: (1) "Describe what a(n) \{\} looks like", (2) "How can you identify a(n) \{\}?", (3) "What does a(n) \{\} look like?" (4) "A caption of an image of a(n) \{\}:" (5) "Describe an image from the internet of a(n) \{\}". Full LLM-prompts for all datasets as well as example image-prompts are given in Sections \ref{sec:FullPrompts} and \ref{sec:imageprompts} of the Appendix. 

    
\section{Related Work}



\subsection{Natural Language Descriptions for Image Classification}
Several prior works use text-based knowledge of image categories to improve classification accuracy. Elhoseiny~\emph{et al. } \cite{Elhoseiny2017LinkTH} extract visual information from unstructured text descriptions collected from the internet to recognize parts of object and classify them in a zero-shot way. Reed~\emph{et al. } \cite{Reed2016LearningDR} and He~\emph{et al. } \cite{He2017FineGrainedIC} use natural language descriptions of bird types to train a multimodal classification model. Huang~\emph{et al. } \cite{Huang2021AttributesGuidedAP} use hand-collected attribute tags to attend over relevant features in images. Tsarfaty~\emph{et al. } \cite{PazArgaman2020ZESTZL} extract visual information from Wikipedia descriptions to enable zero-shot bird classification. Additional works \cite{Shen2022KLITELT, Bujwid2021LargeScaleZI} show improvements on large datasets (e.g., ImageNet) using external information from external databases such as  Imagenet-wiki and Wordnet. While these works show the effectiveness of augmenting zero-shot models with descriptive text, all of these prior works rely on external natural language databases for descriptions. This often limits the possible categories that can be classified and can require extensive preprocessing to extract visual descriptions from noisy natural language. 


\subsection{Generated Text for Downstream Tasks}
Recent work has utilized text generated from LLMs in a number of ways. Santurkar~\emph{et al. } \cite{santurkar2022caption} use an LLM to paraphrase existing image captions to use as data augmentation for CLIP. Liu~\emph{et al. } \cite{Liu2022GeneratedKP} use GPT-3 to generate knowledge on a topic when given a number of demonstrations, which is then used to improve accuracy on common sense reasoning questions. Hu~\emph{et al. } \cite{Hu2022KnowledgeablePI} use a LLM to add labels to text to improve text classification accuracy. In Yu~\emph{et al. } \cite{Yu2022MultimodalKA}, the outputs of a GPT-2 model are used to train an encoder on top of a vision model to generate multimodal image representations for a variety of tasks. Su~\emph{et al. } \cite{Su2022LanguageMC} utilize a language model to perform image captioning by iteritively generating candidate image captions with a LLM and then using feedback from an open vocabulary model to align it to a given image. Similarly, Yang~\emph{et al. } \cite{Yang2022AnES} use GPT-3 along with text descriptions of images for the Visual Question Answering (VQA) task. However, unlike CuPL these prior works are either purely language tasks (common sense reasoning, text classification) or multimodal with some language component (image captioning, VQA). In our work, we demonstrate how LLM generated text can be used to improve purely visual image classification tasks across a number of benchmarks. 


\subsection{Prompt Engineering}
Previous efforts have explored methods for obtaining successful natural language prompts. For both open vocabulary image classification models as well as LLMs, the format of prompts is known to highly affect accuracy \cite{Schick2021ExploitingCF, radford2021learning, brown2020language, gao2020making}. This has led to a large effort to find optimal prompt formats. Proposed methods include crowd-sourcing high performing prompts \cite{Bach2022PromptSourceAI} as well as framing prompts to induce models to give explanations as well as answers \cite{Wei2022ChainOT, Kojima2022LargeLM, Nye2021ShowYW}. Additional works have proposed learning prompts via gradient based methods \cite{Zhang2021DifferentiablePM, Qin2021LearningHT, Li2021PrefixTuningOC, Lester2021ThePO, Shin2020ElicitingKF}, retrieval from a database \cite{Rubin2022LearningTR}, or reformatting/rephrasing existing prompts \cite{Jiang2020HowCW, Rubin2022LearningTR}. 


Most relevant to this work are a number of methods for designing optimal prompts for zero-shot image classification with open vocabulary models. These methods learn prompts formats which yield high accuracy for image classification using either supervised \cite{zhou2022learning, rao2022denseclip} or unsupervised \cite{huang2022unsupervised} methods. However, unlike these prior works this work requires no additional training or labeled data. 
