Miscellaneous Components
Deflation
Introduced by Alayrac et al. in Self-Supervised MultiModal Versatile NetworksDeflation is a video-to-image operation to transform a video network into a network that can ingest a single image. In the two types of video networks considered in the original paper, this deflation corresponds to the following operations: for 3D convolutional based networks, summing the 3D spatio-temporal filters over the temporal dimension to obtain 2D filters; for TSM networks,, turning off the channel shifting which results in a standard residual architecture (ResNet50) for images.
Source: Self-Supervised MultiModal Versatile Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| blind source separation | 1 | 25.00% |
| Action Recognition In Videos | 1 | 25.00% |
| Audio Classification | 1 | 25.00% |
| Self-Supervised Action Recognition | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
