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Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add co-occurrence regularization (ablation) | Deep LSTM · SBU Kinect Interaction | accuracy: 86.03 -> 89.44 | Helped |
| add in-depth LSTM dropout (ablation) | Deep LSTM · SBU Kinect Interaction | accuracy: 86.03 -> 90.10 | Helped |
| in-depth dropout instead of simple dropout (ablation) | Deep LSTM · SBU Kinect Interaction | accuracy: 89.70 -> 90.10 | Helped |
| add co-occurrence on top of in-depth dropout (ablation) | Deep LSTM + In-depth Dropout · SBU Kinect Interaction | accuracy: 90.10 -> 90.41 | Helped |
| add co-occurrence on top of in-depth dropout (ablation) | Deep LSTM + In-depth Dropout · HDM05 | accuracy: 97.25 -> 97.25 | No clear effect |
| add co-occurrence regularization (ablation) | Deep LSTM · CMU | accuracy: 79.53 -> 79.60 | Helped |
| deep LSTM vs hierarchical RNN | skeleton action recognition · CMU | accuracy: 75.02 -> 81.04 | Helped |
Closest papers by meaning
- Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition (CVPR 2016 · similarity 0.92)
- An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition (CVPR 2019 · similarity 0.91)
- An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data (AAAI 2017 · similarity 0.91)
- PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition (CVPR 2020 · similarity 0.90)
- Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition (CVPR 2025 · similarity 0.88)
- Heterogeneous Skeleton-Based Action Representation Learning (CVPR 2025 · similarity 0.87)
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