{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/viphy-probing-visible-physical-commonsense","title":"VIPHY: Probing \"Visible\" Physical Commonsense Knowledge","arxiv_id":"2209.07000","date":"2022-09-15","proceeding":null,"authors":["Shikhar Singh","Ehsan Qasemi","Muhao Chen"],"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 .","url_abs":"https://arxiv.org/abs/2209.07000v1","url_pdf":"https://arxiv.org/pdf/2209.07000v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"viphy-probing-visible-physical-commonsense","repo_url":"https://github.com/axe--/viphy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"visual-reasoning","task_name":"Visual Reasoning"}],"methods":[],"datasets_introduced":[{"slug":"viphy","name":"ViPhy","full_name":"“Visible” Physical Commonsense Knowledge"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}