Summary of Videovista: a Versatile Benchmark For Video Understanding and Reasoning, by Yunxin Li et al.
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
by Yunxin Li, Xinyu Chen, Baotian Hu, Longyue Wang, Haoyuan Shi, Min Zhang
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The paper presents VideoVista, a comprehensive evaluation benchmark for video analysis models. It integrates challenges across diverse content categories, durations, and abilities, comprising 25,000 questions derived from 3,400 videos. The benchmark includes tasks such as anomaly detection, interaction understanding, logical reasoning, and causal reasoning. An automatic data construction framework is introduced to build training data for enhancing the capabilities of video-related large multimodal models (LMMs). A comprehensive evaluation of cutting-edge LMMs reveals that they struggle with fine-grained video tasks involving temporal location, object tracking, and anomaly detection, and exhibit inferior logical and relation reasoning abilities. The results highlight the importance of VideoVista in advancing LMMs that can accurately understand videos and perform precise reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VideoVista is a new way to test how well computers can understand videos. It’s like a big puzzle with 25,000 clues from 3,400 different videos. These videos are about things like “How-to” tutorials, movies, and TV shows, and they’re all different lengths and styles. The test also looks at things like finding weird parts in a video or understanding what’s happening between characters. To make this test possible, the researchers created a special tool that helps them build training data for computers to learn from. When they tested some of the best computer models on VideoVista, they found that these computers are really good at some things, but struggle with others. This is important because it shows us how we can improve these computer models so they can better understand videos and do clever things. |
Keywords
» Artificial intelligence » Anomaly detection » Object tracking
