{"id":1130616,"date":"2025-02-19T09:56:44","date_gmt":"2025-02-19T17:56:44","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=1130616"},"modified":"2025-02-19T09:56:44","modified_gmt":"2025-02-19T17:56:44","slug":"mmworld-towards-multi-discipline-multi-faceted-world-model-evaluation-in-videos","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/mmworld-towards-multi-discipline-multi-faceted-world-model-evaluation-in-videos\/","title":{"rendered":"MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos"},"content":{"rendered":"<p>Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of &#8220;world models&#8221;&#8211; interpreting and reasoning about complex real-world dynamics. To assess these abilities, we posit videos are the ideal medium, as they encapsulate rich representations of real-world dynamics and causalities. To this end, we introduce MMWorld, a new benchmark for multi-discipline, multi-faceted multimodal video understanding. MMWorld distinguishes itself from previous video understanding benchmarks with two unique advantages: (1) multi-discipline, covering various disciplines that often require domain expertise for comprehensive understanding; (2) multi-faceted reasoning, including explanation, counterfactual thinking, future prediction, etc. MMWorld consists of a human-annotated dataset to evaluate MLLMs with questions about the whole videos and a synthetic dataset to analyze MLLMs within a single modality of perception. Together, MMWorld encompasses 1,910 videos across seven broad disciplines and 69 subdisciplines, complete with 6,627 question-answer pairs and associated captions. The evaluation includes 2 proprietary and 10 open-source MLLMs, which struggle on MMWorld (e.g., GPT-4V performs the best with only 52.3\\% accuracy), showing large room for improvement. Further ablation studies reveal other interesting findings such as models&#8217; different skill sets from humans. We hope MMWorld can serve as an essential step towards world model evaluation in videos.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of &#8220;world models&#8221;&#8211; interpreting and reasoning about complex real-world dynamics. To assess these abilities, we posit videos are the ideal medium, as they encapsulate rich representations of real-world dynamics and causalities. To this end, we introduce MMWorld, a new benchmark for multi-discipline, multi-faceted multimodal video understanding. 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