Research map / Video / Video segmentation and tracking
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add recurrent attention to select informative frames (ablation) | CNN+RNN · MARS | Rank@1: 60.3 -> 62.7 | Helped |
| add recurrent attention to select informative frames (ablation) | CNN+RNN · MARS | mAP: 42.0 -> 43.4 | Helped |
| add spatial recurrent processing instead of a fully connected layer (ablation) | CNN+DIFF · MARS | Rank@1: 63.0 -> 64.2 | Helped |
| add spatial recurrent processing instead of a fully connected layer (ablation) | CNN+DIFF · MARS | mAP: 44.8 -> 46.2 | Helped |
| jointly train feature learning and metric learning with TAM and SRM (ablation) | CNN-based video re-identification · MARS | Rank@1: 58.5 -> 70.6 | Helped |
| jointly train feature learning and metric learning with TAM and SRM (ablation) | CNN-based video re-identification · MARS | mAP: 40.3 -> 50.7 | Helped |
| use only color images rather than color images and optical flow | Ours versus McLaughlin et al. · iLIDS-VID | Rank@1: 55.2 vs 58 | Mixed |
Closest papers by meaning
- Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification (CVPR 2020 · similarity 0.92)
- Recurrent Convolutional Network for Video-Based Person Re-Identification (CVPR 2016 · similarity 0.92)
- Temporal-Enhanced Convolutional Network for Person Re-Identification (AAAI 2018 · similarity 0.91)
- Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos (CVPR 2021 · similarity 0.91)
- Spatial and Temporal Mutual Promotion for Video-Based Person Re-Identification (AAAI 2019 · similarity 0.91)
- Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification (AAAI 2018 · similarity 0.90)
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