Summary of Prompting Video-language Foundation Models with Domain-specific Fine-grained Heuristics For Video Question Answering, by Ting Yu et al.
Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering
by Ting Yu, Kunhao Fu, Shuhui Wang, Qingming Huang, Jun Yu
First submitted to arxiv on: 12 Oct 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 The proposed HeurVidQA framework bridges the gap between broad cross-modal knowledge and specific inference demands of Video Question Answering (VideoQA) tasks by leveraging domain-specific entity-action heuristics to refine pre-trained video-language foundation models. This approach treats these models as implicit knowledge engines, employing domain-specific entity-action prompters to direct the model’s focus toward precise cues that enhance reasoning. The method significantly outperforms existing models on multiple VideoQA datasets, demonstrating its importance in integrating domain-specific knowledge into video-language models for more accurate and context-aware VideoQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HeurVidQA is a new way to make computers understand videos better. Right now, computers are not very good at answering questions about what’s happening in a video. This is because they don’t have the right information or skills to do so. To fix this problem, researchers developed HeurVidQA, which helps computers focus on important parts of a video and make connections between different events and objects. This makes it much better at answering questions about videos. The new method was tested on many different video datasets and showed significant improvements over existing methods. |
Keywords
» Artificial intelligence » Inference » Question answering
