132 citations · 520 across the 26 of their papers we have counts for
7 papers · 2 filters
Learnable Boundary Guided Adversarial Training
Jiequan Cui, Shu Liu, Liwei Wang +1
Previous adversarial training raises model robustness under the compromise of accuracy on natural data. In this paper, we reduce natural accuracy degradation. We use the model logi…
Generalized Few-shot Semantic Segmentation
Zhuotao Tian, Xin Lai, Li Jiang +4
Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Seg…
Dive Deeper Into Box for Object Detection
Ran Chen, Yong Liu, Mengdan Zhang +3
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.…
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
Li Jiang, Hengshuang Zhao, Shaoshuai Shi +3
Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this…
3DSSD: Point-based 3D Single Stage Object Detector
Zetong Yang, Yanan Sun, Shu Liu +1
Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a li…
GridMask Data Augmentation
Pengguang Chen, Shu Liu, Hengshuang Zhao +2
We propose a novel data augmentation method `GridMask' in this paper. It utilizes information removal to achieve state-of-the-art results in a variety of computer vision tasks. We…