WFS-SB: Semantic-Boundary Frame Selection

Long-video understanding is not only a context-length problem. If a system feeds too many redundant frames into a multimodal model, inference becomes expensive and important transitions can be diluted by noise.

WFS-SB approaches the problem through semantic boundaries. The method uses wavelet-based signals to identify meaningful changes in video structure, then selects frames that preserve the reasoning-relevant parts of the video.

Method and motivation

• It reduces redundant visual input before expensive large vision-language model (LVLM) inference.
• It focuses the model on event transitions and semantic boundaries.
• It improves long-video inference efficiency without relying only on larger context windows.
• It connects naturally with event-anchored frame selection work.

Links

• arXiv
• Code