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 |
---|
Task | Papers | Share |
---|---|---|
Action Recognition | 20 | 4.43% |
Time Series Analysis | 14 | 3.10% |
Video Generation | 13 | 2.88% |
Temporal Action Localization | 9 | 2.00% |
Activity Recognition | 8 | 1.77% |
Graph Attention | 8 | 1.77% |
Video Understanding | 8 | 1.77% |
Denoising | 7 | 1.55% |
Language Modelling | 6 | 1.33% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |