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Have vision-language models achieved human-level compositional reasoning? Our research suggests: not quite yet. We’re excited to present CREPE – a large-scale Compositional REPresentation Evaluation benchmark for vision-language models – as a 🌟highlight🌟at #CVPR2023. 🧵1/7

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Zixian Ma

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21,382 просмотров • 3 лет назад •via X (Twitter)

#CVPR2023
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Фото профиля Zixian Ma
Zixian Ma3 лет назад

🧵2/7 We are the amazing team of @zixianma02, @jhoer100, @momergul_, @monagandhi09, @irena_gao, and @RanjayKrishna. Kudos to @jhoer100 and @amengta for their designs 🫶 Check out our paper and code for more details! Paper: Code:

Фото профиля Zixian Ma
Zixian Ma3 лет назад

🧵3/7 CREPE measures two important aspects of compositionality identified in cognitive science literature: systematicity and productivity.

Фото профиля Zixian Ma
Zixian Ma3 лет назад

🧵4/7 Systematicity evaluates how well a model generalizes to unseen compositions.

Фото профиля Zixian Ma
Zixian Ma3 лет назад

🧵5/7 Productivity tests how well a model comprehends increasingly complex scenes and expressions.

Фото профиля Zixian Ma
Zixian Ma3 лет назад

🧵6/7 We devise two separate pipelines to generate test datasets for systematicity and productivity. Our datasets consist of a large quantity of image-text pairs and hard negative captions for image-to-text retrieval evaluation.

Фото профиля Zixian Ma
Zixian Ma3 лет назад

🧵7/7 Our evaluation of diverse VLMs shows that model performance degrades when compositions are unseen or when complexity increases, suggesting: 1. SOTA VLMs exhibit little systematicity or productivity; 2. Neither emerges as we scale up the pretraining dataset or model size.

Фото профиля harpreet
harpreet3 лет назад

Looking forward to chatting with you all about the paper on the podcast!

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