QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
Video understanding is often plagued by severe temporal redundancy, where processing dense frame sequences is both semantically inefficient and computationally expensive. This challenge is further amplified when only a small subset of frames is truly relevant to the given query. In this paper, we propose a Query- and Content-Aware (QCA) keyframe selection framework that can select a compact yet information-rich set of frames from long videos. QCA first partitions the video into temporal segments and estimates the information contribution of each segment by jointly modeling query relevance and
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorJun Peng →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- Linked via arxiv authorBaiyang Song →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- Linked via arxiv authorJie Li →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- Linked via arxiv authorHui Li →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- Linked via arxiv authorYiyi Zhou →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- Linked via arxiv authorRongrong Ji →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
- Linked via arxiv authorYonghong Tian →
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding