Summary of Vinoground: Scrutinizing Lmms Over Dense Temporal Reasoning with Short Videos, by Jianrui Zhang et al.
Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
by Jianrui Zhang, Mu Cai, Yong Jae Lee
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate whether modern large multimodal models (LMMs) have effectively addressed the challenges of short video comprehension. The study reveals that LMMs still lack fundamental reasoning capabilities when dealing with short videos. To evaluate these models, the authors introduce Vinoground, a temporal counterfactual benchmark comprising 1000 short and natural video-caption pairs. Results show that existing LMMs struggle to distinguish between different actions and object transformations, with the best model GPT-4o achieving only around 50% accuracy on text and video scores, compared to the human baseline of around 90%. The study highlights the need for further research into temporal reasoning in short videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LMMs are super smart computers that can understand lots of things, like pictures and words. But they’re not perfect yet! Some people thought they could already understand very short videos, but this study shows that’s not true. The researchers made a special test called Vinoground to see how well these models do. They found out that even the best model gets only about half of the answers correct, which is way worse than humans who get around 90% correct! This means we still have lots to learn and improve before these computers can really understand short videos. |
Keywords
* Artificial intelligence * Gpt
