Summary of Top-down Activity Representation Learning For Video Question Answering, by Yanan Wang and Shuichiro Haruta and Donghuo Zeng and Julio Vizcarra and Mori Kurokawa
Top-down Activity Representation Learning for Video Question Answering
by Yanan Wang, Shuichiro Haruta, Donghuo Zeng, Julio Vizcarra, Mori Kurokawa
First submitted to arxiv on: 12 Sep 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 This paper proposes a new approach to video question answering (VideoQA) that can capture complex hierarchical human activities in videos. Recent multimodal models have improved their temporal reasoning capabilities, but they often struggle with contextual events that are not continuously distributed over short sequences. To address this issue, the authors convert long-term video sequences into a spatial image domain and fine-tune the LLaVA model for the VideoQA task. The approach achieves competitive performance on the STAR task and exceeds the state-of-the-art score by 2.8 points on the NExTQA task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video question answering is important because it can help computers understand what’s happening in videos. Right now, computers are good at understanding short sequences of actions, but they struggle with longer events that happen over time. To solve this problem, scientists converted long videos into a special format and used a special computer model to answer questions about the video. This new approach is really good at answering questions about what’s happening in videos! |
Keywords
» Artificial intelligence » Question answering
