FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding

Zhuo Cao, Bingqing Zhang, Heming Du, Xin Yu, Xue Li, Sen Wang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9208-9218

Text-guided Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on textual descriptions encompassing two subtasks: Moment Retrieval (MR) and Highlight Detection (HD). Although previous typical methods have achieved commendable results it is still challenging to retrieve short video moments. This is primarily due to the reliance on sparse and limited decoder queries which significantly constrain the accuracy of predictions. Furthermore suboptimal outcomes often arise because previous methods rank predictions based on isolated predictions neglecting the broader video context. To tackle these issues we introduce FlashVTG a framework featuring a Temporal Feature Layering (TFL) module and an Adaptive Score Refinement (ASR) module. The TFL module replaces the traditional decoder structure to capture 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. Extensive experiments demonstrate that FlashVTG achieves state-of-the-art performance on four widely adopted datasets in both MR and HD. Specifically on the QVHighlights dataset it boosts mAP by 5.8% for MR and 3.3% for HD. For short-moment retrieval FlashVTG increases mAP to 125% of previous SOTA performance. All these improvements are made without adding training burdens underscoring its effectiveness.