Summary of Videoqa in the Era Of Llms: An Empirical Study, by Junbin Xiao et al.
VideoQA in the Era of LLMs: An Empirical Study
by Junbin Xiao, Nanxin Huang, Hangyu Qin, Dongyang Li, Yicong Li, Fengbin Zhu, Zhulin Tao, Jianxing Yu, Liang Lin, Tat-Seng Chua, Angela Yao
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Video Large Language Models (Video-LLMs) have made significant advancements in various video-language tasks. This study focuses on understanding the behavior of these models in Video Question Answering (VideoQA), a crucial task for developing more human-like video understanding and question answering capabilities. The results show that Video-LLMs excel in VideoQA by correlating contextual cues and generating plausible responses to questions about varied video contents. However, they struggle with handling video temporality, both in reasoning about temporal content ordering and grounding QA-relevant temporal moments. Additionally, the models are unresponsive to adversarial video perturbations while being sensitive to simple variations of candidate answers and questions. This study highlights the urgent need for developing rationales in Video-LLM development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video Large Language Models (Video-LLMs) have improved many tasks involving videos and words. In this study, we looked at how well these models do on a special task called Video Question Answering (VideoQA). This is important because it helps us understand how to make computers better at understanding videos and answering questions about them. The results show that the models are good at VideoQA because they can connect ideas in the video and come up with smart answers. However, they struggle with understanding the order of events in a video or pinpointing specific moments in time. We also found that these models don’t do well when faced with fake videos, but they are very sensitive to small changes in questions and answers. This study shows us where we need to improve our computer systems to make them better at understanding videos. |
Keywords
» Artificial intelligence » Grounding » Question answering
