790 citations · 2.7k across the 36 of their papers we have counts for
37 papers
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…
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Yu-Huan Wu, Da Zhang, Le Zhang +4
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners. To this end, we introdu…
Zero Pixel Directional Boundary by Vector Transform
Edoardo Mello Rella, Ajad Chhatkuli, Yun Liu +2
Boundaries are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the label representation, which typically l…
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…
Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net
Yu Qiu, Yun Liu, Le Zhang +1
Existing salient object detection (SOD) methods mainly rely on U-shaped convolution neural networks (CNNs) with skip connections to combine the global contexts and local spatial de…
P2T: Pyramid Pooling Transformer for Scene Understanding
Yu-Huan Wu, Yun Liu, Xin Zhan +1
Recently, the vision transformer has achieved great success by pushing the state-of-the-art of various vision tasks. One of the most challenging problems in the vision transformer…
Rethinking Global Context in Crowd Counting
Guolei Sun, Yun Liu, Thomas Probst +3
This paper investigates the role of global context for crowd counting. Specifically, a pure transformer is used to extract features with global information from overlapping image p…
MobileSal: Extremely Efficient RGB-D Salient Object Detection
Yu-Huan Wu, Yun Liu, Jun Xu +3
The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this paper int…
EDN: Salient Object Detection via Extremely-Downsampled Network
Yu-Huan Wu, Yun Liu, Le Zhang +2
Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and…
DOTS: Decoupling Operation and Topology in Differentiable Architecture Search
Yu-Chao Gu, Li-Juan Wang, Yun Liu +4
Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search an…
Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow
Yu-Chao Gu, Le Zhang, Yun Liu +2
Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes by transferring knowledge from semantic descriptions to visual representations. Recent generati…
Regularized Densely-connected Pyramid Network for Salient Instance Segmentation
Yu-Huan Wu, Yun Liu, Le Zhang +2
Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we pro…
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Shijie Li, Xieyuanli Chen, Yun Liu +3
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational reso…
Rethinking 3D LiDAR Point Cloud Segmentation
Shijie Li, Yun Liu, Juergen Gall
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in…
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Shijie Li, Yazan Abu Farha, Yun Liu +2
With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos.…
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
Yu Qiu, Yun Liu, Shijie Li +1
The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentat…
Ordered or Orderless: A Revisit for Video based Person Re-Identification
Le Zhang, Zenglin Shi, Joey Tianyi Zhou +5
Is recurrent network really necessary for learning a good visual representation for video based person re-identification (VPRe-id)? In this paper, we first show that the common pra…
AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning
Xin-Yu Zhang, Le Zhang, Zao-Yi Zheng +3
Triplet loss has been widely employed in a wide range of computer vision tasks, including local descriptor learning. The effectiveness of the triplet loss heavily relies on the tri…
An Evaluation of Feature Matchers for Fundamental Matrix Estimation
Jia-Wang Bian, Yu-Huan Wu, Ji Zhao +4
Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSA…
Scoot: A Perceptual Metric for Facial Sketches
Deng-Ping Fan, ShengChuan Zhang, Yu-Huan Wu +5
Human visual system has the strong ability to quick assess the perceptual similarity between two facial sketches. However, existing two widely-used facial sketch metrics, e.g., FSI…