ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding

Understand

Video temporal grounding is an emerging topic aiming to identify specific clips within videos.

• In addition to pre-trained video models, contemporary methods utilize pre-trained vision-language models (VLM) to capture detailed characteristics of diverse scenes and objects from video frames.
• However, as pre-trained on images, VLM may struggle to distinguish action-sensitive patterns from static objects, making it necessary to adapt them to specific data domains for effective feature representation over temporal grounding.
• We address two primary challenges to achieve this goal.