Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

• URL: http://arxiv.org/abs/2011.12450v2
• Date: Mon, 26 Apr 2021 14:20:03 GMT
• Title: Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
• Authors: Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, Ping Luo
• Abstract summary: We present Sparse R-CNN, a purely sparse method for object detection in images. Final predictions are directly output without non-maximum suppression post-procedure. We hope our work could inspire re-thinking the convention of dense prior in object detectors.
• Score: 77.9701193170127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 as $k$ anchor boxes pre-defined on all grids of image feature map of size $H\times W$. In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location. By eliminating $HWk$ (up to hundreds of thousands) hand-designed object candidates to $N$ (e.g. 100) learnable proposals, Sparse R-CNN completely avoids all efforts related to object candidates design and many-to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard $3\times$ training schedule and running at 22 fps using ResNet-50 FPN model. We hope our work could inspire re-thinking the convention of dense prior in object detectors. The code is available at: https://github.com/PeizeSun/SparseR-CNN.

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