ausblenden:
Schlagwörter:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Zusammenfassung:
Video Large Language Models (Vid-LLMs) have made remarkable advancements in
comprehending video content for QA dialogue. However, they struggle to extend
this visual understanding to tasks requiring precise temporal localization,
known as Video Temporal Grounding (VTG). To address this gap, we introduce
Number-Prompt (NumPro), a novel method that empowers Vid-LLMs to bridge visual
comprehension with temporal grounding by adding unique numerical identifiers to
each video frame. Treating a video as a sequence of numbered frame images,
NumPro transforms VTG into an intuitive process: flipping through manga panels
in sequence. This allows Vid-LLMs to "read" event timelines, accurately linking
visual content with corresponding temporal information. Our experiments
demonstrate that NumPro significantly boosts VTG performance of top-tier
Vid-LLMs without additional computational cost. Furthermore, fine-tuning on a
NumPro-enhanced dataset defines a new state-of-the-art for VTG, surpassing
previous top-performing methods by up to 6.9\% in mIoU for moment retrieval and
8.5\% in mAP for highlight detection. The code will be available at
https://github.com/yongliang-wu/NumPro.
