230 citations · 804 across the 33 of their papers we have counts for
8 papers · 1 filter
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.…
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks
Shu Liu, Wei Li, Yunfang Wu +2
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them. Both the previous pipeline a…
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…
DSGN: Deep Stereo Geometry Network for 3D Object Detection
Yilun Chen, Shu Liu, Xiaoyong Shen +1
Most state-of-the-art 3D object detectors heavily rely on LiDAR sensors because there is a large performance gap between image-based and LiDAR-based methods. It is caused by the wa…