PnP, or Poll and Pool, is sampling module extension for DETR-type architectures that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result.
Source: PnP-DETR: Towards Efficient Visual Analysis with TransformersPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Denoising | 23 | 14.11% |
Pose Estimation | 19 | 11.66% |
Deblurring | 9 | 5.52% |
Image Reconstruction | 9 | 5.52% |
Image Restoration | 8 | 4.91% |
Super-Resolution | 7 | 4.29% |
Object | 7 | 4.29% |
Image Deblurring | 5 | 3.07% |
6D Pose Estimation using RGB | 4 | 2.45% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |