VIPHY: Probing "Visible" Physical Commonsense Knowledge

• URL: http://arxiv.org/abs/2209.07000v1
• Date: Thu, 15 Sep 2022 02:06:25 GMT
• Title: VIPHY: Probing "Visible" Physical Commonsense Knowledge
• Authors: Shikhar Singh, Ehsan Qasemi, Muhao Chen
• Abstract summary: Vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks. We evaluate their ability to acquire "visible" physical knowledge. Our results indicate a severe gap between model and human performance.
• Score: 22.00069189468524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks (e.g. attributes, location). While such tasks measure the requisite knowledge to ground and reason over a given visual instance, they do not, however, measure the ability of VLMs to retain and generalize such knowledge. In this work, we evaluate their ability to acquire "visible" physical knowledge -- the information that is easily accessible from images of static scenes, particularly across the dimensions of object color, size and space. We build an automatic pipeline to derive a comprehensive knowledge resource for calibrating and probing these models. Our results indicate a severe gap between model and human performance across all three tasks. Furthermore, our caption pretrained baseline (CapBERT) significantly outperforms VLMs on both size and spatial tasks -- highlighting that despite sufficient access to ground language with visual modality, they struggle to retain such knowledge. The dataset and code are available at https://github.com/Axe--/ViPhy .

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