Focal Sparse Convolutional Networks Focal Sparse Conv计算 Cubic importance prediction: 对输入,使用一个submainfold sparse conv+sigmoid预测输出特征图位置的权重。这里submainfolds sparse conv 输出维度是27,代表在输出特征图上,以Pin的每个有效点为中心的3✖️3✖️3区域的重要性。 Important input selectio...
《Focal Sparse Convolutional Networks for 3D Object Detection》(CVPR 2022) GitHub: github.com/dvlab-research/FocalsConv [fig1]《Integrative Few-Shot Learningfor Classification and Segmentation》(CVPR 2022) GitHub: github.com/dahyun-kang/ifsl [fig6]...
@inproceedings{focalsconv-chen, title={Focal Sparse Convolutional Networks for 3D Object Detection}, author={Chen, Yukang and Li, Yanwei and Zhang, Xiangyu and Sun, Jian and Jia, Jiaya}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2022} }...
3d semantic segmentation with submanifold sparse convolutional networks. 2018. 2 [48] Guangsheng Shi, Ruifeng Li, and Chao Ma. Pillarnet: Real- time and high-performance pillar-based 3d object detection. In Computer Vision–ECCV 2022: 17th European Confer- ence, Tel ...
特征金字塔的不同层可以检测不同尺度的 object。FPN 相比 Fully Convolutional Network(FCN)提高了多尺度预测能力,这个提高见以下研究:RPN [28] 和 DeepMask-style proposal [34] 及其它的 two-stage 检测算法(Fast R-CNN [10] 或 Mask R-CNN [14])的研究。
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have...
This paper illustrates a completely unique technique of multi-focus image fusion involving Stationary Wavelet Transform (SWT) and Convolutional Sparse Representation (CSR). Sparse-based fusion strategies do not retain information representation and cannot tolerate minor mistakes in registration. The SWT ...
As popularized in the R-CNN framework [11], the first stage generates a sparse set of candidate object locations and the second stage classifies each candidate location as one of the foreground classes or as background using a convolutional neural net- work. Through a sequence of advances [10...
登录 摘要 The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object loca...
The focal loss is designed to address class imbalance by down-weighting inliers (easy examples) such that their contribution to the total loss is small even if their number is large. It focuses on training a sparse set of hard examples. ...