Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos

• URL: http://arxiv.org/abs/2410.02763v1
• Date: Thu, 3 Oct 2024 17:59:58 GMT
• Title: Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
• Abstract summary: We introduce Vinoground, a temporal counterfactual LMM evaluation benchmark encompassing 1000 short and natural video-caption pairs. We demonstrate that existing LMMs severely struggle to distinguish temporal differences between different actions and object transformations. All open-source multimodal models and CLIP-based models perform much worse, producing mostly random chance performance.
• Score: 30.72753471355853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There has been growing sentiment recently that modern large multimodal models (LMMs) have addressed most of the key challenges related to short video comprehension. As a result, both academia and industry are gradually shifting their attention towards the more complex challenges posed by understanding long-form videos. However, is this really the case? Our studies indicate that LMMs still lack many fundamental reasoning capabilities even when dealing with short videos. We introduce Vinoground, a temporal counterfactual LMM evaluation benchmark encompassing 1000 short and natural video-caption pairs. We demonstrate that existing LMMs severely struggle to distinguish temporal differences between different actions and object transformations. For example, the best model GPT-4o only obtains ~50% on our text and video scores, showing a large gap compared to the human baseline of ~90%. All open-source multimodal models and CLIP-based models perform much worse, producing mostly random chance performance. Through this work, we shed light onto the fact that temporal reasoning in short videos is a problem yet to be fully solved. The dataset and evaluation code are available at https://vinoground.github.io.

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