312 citations · 1.5k across the 54 of their papers we have counts for
9 papers · 1 filter
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao +8
Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution a…
Point Transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia +2
Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection.…
Fully Convolutional Networks for Panoptic Segmentation
Yanwei Li, Hengshuang Zhao, Xiaojuan Qi +4
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foregrou…
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…
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
Zhuotao Tian, Hengshuang Zhao, Michelle Shu +3
State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation…
Exploring Self-attention for Image Recognition
Hengshuang Zhao, Jiaya Jia, Vladlen Koltun
Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness f…
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…
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation
Xiaogang Xu, Hengshuang Zhao, Jiaya Jia
Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task. The effect of this type…
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…