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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

Zixian Ma

1,268 subscribers

21,382 views • 3 years ago •via X (Twitter)

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Zixian Ma3 years ago

🧵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:

Zixian Ma3 years ago

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

Zixian Ma3 years ago

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

Zixian Ma3 years ago

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

Zixian Ma3 years ago

🧵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.

Zixian Ma3 years ago

🧵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.

harpreet3 years ago

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

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