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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- LinkedLinked via unknownVG-GUI-TASKER/VG-GUI-TASKER →
- LinkedLinked via arxiv author · 85%Jun Peng →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”
- LinkedLinked via arxiv author · 85%Baiyang Song →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”
- LinkedLinked via arxiv author · 85%Jie Li →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”
- LinkedLinked via arxiv author · 85%Hui Li →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”
- LinkedLinked via arxiv author · 85%Yiyi Zhou →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”
- LinkedLinked via arxiv author · 85%Rongrong Ji →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”
- LinkedLinked via arxiv author · 85%Yonghong Tian →
“QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding”