ConTrans: Learning Text-enhanced Local-global Temporal Representations for Zero-shot Temporal Action Localization
📰 ArXiv cs.AI
Learn how ConTrans enhances local-global temporal representations for zero-shot temporal action localization in videos, improving detection and location of unseen actions
Action Steps
- Apply ConTrans to your existing ZS-TAL model to enhance local-global temporal representations
- Use text-enhanced features to improve feature representation capabilities
- Configure your model to focus on relative-offset-based local correlations between video frames
- Test your model on untrimmed videos to evaluate its performance on unseen actions
- Compare the results with existing approaches to measure the improvement
Who Needs to Know This
Computer vision engineers and researchers working on action localization tasks can benefit from this approach to improve their models' performance and accuracy
Key Insight
💡 ConTrans improves zero-shot temporal action localization by modeling local correlations and enhancing feature representation capabilities
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🚀 ConTrans: Enhancing local-global temporal representations for zero-shot temporal action localization in videos! 📹
Key Takeaways
Learn how ConTrans enhances local-global temporal representations for zero-shot temporal action localization in videos, improving detection and location of unseen actions
Full Article
Title: ConTrans: Learning Text-enhanced Local-global Temporal Representations for Zero-shot Temporal Action Localization
Abstract:
arXiv:2605.30689v1 Announce Type: cross Abstract: Zero-shot Temporal Action Localization (ZS-TAL) aims to detect and locate previously unseen actions in untrimmed videos. However, existing approaches primarily focus on modeling long-range contextual information, often neglecting the critical relative-offset-based local correlations between video frames. Furthermore, their performance is hindered by limited feature representation capabilities due to the shallow nature of their network architectur
Abstract:
arXiv:2605.30689v1 Announce Type: cross Abstract: Zero-shot Temporal Action Localization (ZS-TAL) aims to detect and locate previously unseen actions in untrimmed videos. However, existing approaches primarily focus on modeling long-range contextual information, often neglecting the critical relative-offset-based local correlations between video frames. Furthermore, their performance is hindered by limited feature representation capabilities due to the shallow nature of their network architectur
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