Temporal attention can be seen as a dynamic time selection mechanism determining when to pay attention, and is thus usually used for video processing.
Source: Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-IdentificationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Action Recognition | 20 | 4.47% |
Time Series Analysis | 14 | 3.13% |
Video Generation | 12 | 2.68% |
Temporal Action Localization | 9 | 2.01% |
Activity Recognition | 8 | 1.79% |
Graph Attention | 8 | 1.79% |
Video Understanding | 8 | 1.79% |
Denoising | 7 | 1.57% |
Language Modelling | 6 | 1.34% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |