Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
| Autor: | Rufeng Zhang, Chenfeng Xu, Peize Sun, Wei Zhan, Tao Kong, Lei Li, Ping Luo, Masayoshi Tomizuka, Changhu Wang, Yi Jiang, Zehuan Yuan |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Schedule Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Cognitive neuroscience of visual object recognition Computer Science - Computer Vision and Pattern Recognition Pattern recognition Object (computer science) Object detection Set (abstract data type) Feature (computer vision) Pattern recognition (psychology) Code (cryptography) Artificial intelligence business |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.2011.12450 |
| Popis: | 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. Comment: add test-dev; add crowdhuman |
| Databáze: | OpenAIRE |
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