109 citations · 144 across the 9 of their papers we have counts for
21 papers · 1 filter
Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection
Yuhan Gao, Xinqing Li, Xin He +4
Edge detection is a fundamental image analysis task that underpins numerous high-level vision applications. Recent advances in Transformer architectures have significantly improved…
Low-Resolution Self-Attention for Semantic Segmentation
Yu-Huan Wu, Shi-Chen Zhang, Yun Liu +6
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction. While existing vision tra…
Revisiting Computer-Aided Tuberculosis Diagnosis
Yun Liu, Yu-Huan Wu, Shi-Chen Zhang +3
Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Although early diagnosis and treatment can greatly improve the chances of survival, it remai…
RGB-D Indiscernible Object Counting in Underwater Scenes
Guolei Sun, Xiaogang Cheng, Zhaochong An +5
Recently, indiscernible/camouflaged scene understanding has attracted lots of research attention in the vision community. We further advance the frontier of this field by systemati…
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
Yun Liu, Xuefeng Yan, Zhilei Chen +3
Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to th…
Boosting Few-shot Semantic Segmentation with Transformers
Guolei Sun, Yun Liu, Jingyun Liang +1
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentati…