{"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/perception-test-a-diagnostic-benchmark-for-2","title":"Perception Test: A Diagnostic Benchmark for Multimodal Video Models","arxiv_id":"2305.13786","date":"2023-05-23","proceeding":"NeurIPS 2023 11","authors":["Viorica Pătrăucean","Lucas Smaira","Ankush Gupta","Adrià Recasens Continente","Larisa Markeeva","Dylan Banarse","Skanda Koppula","Joseph Heyward","Mateusz Malinowski","Yi Yang","Carl Doersch","Tatiana Matejovicova","Yury Sulsky","Antoine Miech","Alex Frechette","Hanna Klimczak","Raphael Koster","Junlin Zhang","Stephanie Winkler","Yusuf Aytar","Simon Osindero","Dima Damen","Andrew Zisserman","João Carreira"],"abstract":"We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, SeViLA, or GPT-4). Compared to existing benchmarks that focus on computational tasks (e.g. classification, detection or tracking), the Perception Test focuses on skills (Memory, Abstraction, Physics, Semantics) and types of reasoning (descriptive, explanatory, predictive, counterfactual) across video, audio, and text modalities, to provide a comprehensive and efficient evaluation tool. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or limited finetuning regime. For these purposes, the Perception Test introduces 11.6k real-world videos, 23s average length, designed to show perceptually interesting situations, filmed by around 100 participants worldwide. The videos are densely annotated with six types of labels (multiple-choice and grounded video question-answers, object and point tracks, temporal action and sound segments), enabling both language and non-language evaluations. The fine-tuning and validation splits of the benchmark are publicly available (CC-BY license), in addition to a challenge server with a held-out test split. Human baseline results compared to state-of-the-art video QA models show a substantial gap in performance (91.4% vs 46.2%), suggesting that there is significant room for improvement in multimodal video understanding. Dataset, baseline code, and challenge server are available at https://github.com/deepmind/perception_test","url_abs":"https://arxiv.org/abs/2305.13786v2","url_pdf":"https://arxiv.org/pdf/2305.13786v2.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":"perception-test-a-diagnostic-benchmark-for-2","repo_url":"https://github.com/deepmind/perception_test","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"grounded-video-question-answering","task_name":"Grounded Video Question Answering"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"point-tracking","task_name":"Point Tracking"},{"task_slug":"sound-event-localization-and-detection","task_name":"Sound Event Localization and Detection"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"video-question-answering","task_name":"Video Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-tracking-on-perception-test","task":"Object Tracking","dataset":"Perception Test","model":"Siam-FC","rank_in_archive_order":1,"of":1,"metrics":{"Average IOU":"0.66"},"uses_additional_data":false},{"leaderboard":"/sota/point-tracking-on-perception-test","task":"Point Tracking","dataset":"Perception Test","model":"Static Baseline","rank_in_archive_order":1,"of":1,"metrics":{"Average Jaccard":"0.36"},"uses_additional_data":false},{"leaderboard":"/sota/video-question-answering-on-perception-test","task":"Video Question Answering","dataset":"Perception Test","model":"Flamingo","rank_in_archive_order":6,"of":6,"metrics":{"Accuracy (Top-1)":"0.46"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.13786","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}