Summary of Flashvtg: Feature Layering and Adaptive Score Handling Network For Video Temporal Grounding, by Zhuo Cao et al.
FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding
by Zhuo Cao, Bingqing Zhang, Heming Du, Xin Yu, Xue Li, Sen Wang
First submitted to arxiv on: 18 Dec 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 Text-guided Video Temporal Grounding (VTG) is a machine learning framework that localizes relevant segments in untrimmed videos based on textual descriptions. The task involves two subtasks: Moment Retrieval (MR) and Highlight Detection (HD). Traditional methods have achieved good results, but it’s challenging to retrieve short video moments due to the reliance on limited decoder queries. To tackle this issue, we introduce FlashVTG, a framework featuring Temporal Feature Layering (TFL) and Adaptive Score Refinement (ASR) modules. The TFL module captures nuanced video content variations across multiple temporal scales, while the ASR module improves prediction ranking by integrating context from adjacent moments and multi-temporal-scale features. FlashVTG achieves state-of-the-art performance on four datasets in both MR and HD, with a 5.8% boost in mAP for QVHighlights dataset. For short-moment retrieval, it increases mAP to 125% of previous SOTA performance without adding training burdens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using text to find specific moments in videos. It’s like searching for a clip in a long movie based on what someone tells you. The current methods are good, but they struggle when looking for short moments. To solve this problem, the researchers created a new way of doing it called FlashVTG. This method looks at different parts of the video and combines them to find the right moment. It works really well and is better than other methods that have been tried before. The results are impressive, with improvements in finding short moments and detecting highlights. |
Keywords
» Artificial intelligence » Decoder » Grounding » Machine learning
