Temporal Cross-Layer Correlation Mining for Action Recognition
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Multimedia, 2022, 24, pp. 668-676
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Temporal_Cross-Layer_Correlation_Mining_for_Action_Recognition.pdf | Published version | 1.81 MB |
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Neighboring frames are more correlated compared to frames from further temporal distances. In this paper, we aim to explore the temporal correlations among neighboring frames and exploit cross-layer multi-scale features for action recognition. First, we present a Temporal Cross-Layer Correlation (TCLC) framework for temporal correlation learning. The unified framework uncovers both local and global structures from video data, enabling a better exploration of temporal context and assisting cross-layer spatio-temporal feature learning. Second, we propose a novel cross-layer attention and a center-guided attention mechanism to integrate features with contextual knowledge from multiple scales. Our method is a two-stage process for effective cross-layer feature learning. The first stage incorporates the cross-layer attention module to decide the importance weight of the convolutional layers. The second stage leverages the center-guided attention mechanism to aggregate local features from each layer for the generation of a final video representation. We leverage global centers to extract shared semantic knowledge among videos. We evaluate TCLC on three action recognition datasets, i.e., UCF-101, HMDB-51 and Kinetics. Our experimental results demonstrate the superiority of our proposed temporal correlation mining method.
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