Counting Grid Aggregation for Event Retrieval and Recognition
Zhanning Gao, Gang Hua, Dongqing Zhang, Jianru Xue, Nanning Zheng

TL;DR
This paper introduces a counting grid model that aggregates deep features across video frames to create a compact, discriminative representation for event retrieval and recognition, outperforming existing methods.
Contribution
The paper proposes a novel spatially consistent counting grid model that effectively aggregates deep features across frames, reducing redundancy and improving accuracy in event recognition.
Findings
Achieves significantly better accuracy than existing methods.
Produces a more compact representation for videos.
Effectively identifies and removes feature redundancy.
Abstract
Event retrieval and recognition in a large corpus of videos necessitates a holistic fixed-size visual representation at the video clip level that is comprehensive, compact, and yet discriminative. It shall comprehensively aggregate information across relevant video frames, while suppress redundant information, leading to a compact representation that can effectively differentiate among different visual events. In search for such a representation, we propose to build a spatially consistent counting grid model to aggregate together deep features extracted from different video frames. The spatial consistency of the counting grid model is achieved by introducing a prior model estimated from a large corpus of video data. The counting grid model produces an intermediate tensor representation for each video, which automatically identifies and removes the feature redundancy across the different…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Human Pose and Action Recognition · Multimodal Machine Learning Applications
