Summary of Task-driven Exploration: Decoupling and Inter-task Feedback For Joint Moment Retrieval and Highlight Detection, by Jin Yang et al.
Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection
by Jin Yang, Ping Wei, Huan Li, Ziyang Ren
First submitted to arxiv on: 14 Apr 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 A novel task-driven top-down framework called TaskWeave is proposed for joint moment retrieval and highlight detection in videos. The framework introduces a task-decoupled unit to capture task-specific and common representations, and an inter-task feedback mechanism that transforms the results of one task into guiding masks for the other task. A task-dependent joint loss function is used to optimize the model. Comprehensive experiments on QVHighlights, TVSum, and Charades-STA datasets demonstrate the effectiveness and flexibility of TaskWeave. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Videos are becoming increasingly important in our daily lives, but understanding them is a challenging task. This paper presents a new way to find specific moments in videos and highlight what’s important. The approach uses a combination of techniques to improve performance and adapt to different situations. Results show that this method outperforms existing methods on several datasets. |
Keywords
» Artificial intelligence » Loss function
