Nov 25, 2020 · We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates.
Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well- established detector baselines on the challenging COCO.
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals. License: MIT. Paper (CVPR 2021). Sparse R-CNN: End-to-End Object Detection with Learnable ...
Jul 4, 2023 · In this paper, we present Sparse R-CNN, a very simple and sparse method for object detection in images.
Sparse R-CNN is a purely sparse method for object detection in images, without object positional candidates enumerating on all(dense) image grids.
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Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well- established detector baselines on the challenging COCO.
This paper rethinks the necessity of dense priors (either anchor boxes or reference points) in object detection, very similar to TSP. Sparse RCNN uses a number ...
Nov 29, 2021 · Written by Richard Xu. A discussion of the paper titled “Sparse R-CNN: End-to-End Object Detection with Learnable Proposals”.
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such ...
R-CNN (Girshick et al., 2014) is the pioneering two-stage detector that performs detection on the features of region proposals produced by selective search. ...