方法对比
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| Mask R-CNN：具有像素级掩码的实例分割× | Faster R-CNN× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2015 |
| 提出者≠ | Kaiming He et al. (FAIR) | Ren, S.; He, K.; Girshick, R.; Sun, J. (Microsoft Research) |
| 类型≠ | Instance segmentation deep neural network | Two-stage object detection CNN |
| 开创性文献≠ | He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2980–2988. DOI ↗ | Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91–99. link ↗ |
| 别名≠ | Mask Region-based Convolutional Neural Network, Instance Segmentation R-CNN, He et al. 2017 Segmentation Model, Maske R-CNN | Faster RCNN, Faster-RCNN, RPN-based detector, two-stage object detector |
| 相关 | 2 | 2 |
| 摘要≠ | Mask R-CNN is a deep learning framework for instance segmentation introduced by Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick at Facebook AI Research (FAIR) in 2017. It extends Faster R-CNN by adding a parallel branch that predicts a binary pixel-level mask for each detected object instance, enabling simultaneous object detection, classification, and fine-grained segmentation in a single forward pass. | Faster R-CNN is a two-stage deep convolutional object detection framework introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun (Microsoft Research) at NeurIPS 2015. It replaces the slow selective-search region proposal step used in its predecessors R-CNN and Fast R-CNN with a learned Region Proposal Network (RPN) that shares convolutional features with the detection head, enabling the first end-to-end trainable, near-real-time accurate object detector and establishing a long-standing accuracy benchmark on PASCAL VOC and MS COCO. |
| ScholarGate数据集 ↗ |
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