Yun Liu   I am a senior scientist at Institute for Infocomm Research (I2R), A*STAR. Before that, I was a postdoc in Computer Vision Lab @ ETH Zurich, working with Prof. Luc Van Gool. Under the supervision of Prof. Ming-Ming Cheng, I obtained my bachelor's degree and my doctoral degree from Nankai University in 2016 and 2020, respectively. My research interests include computer vision and machine learning (especially deep learning).   VAGRANTLYUN [AT] GMAIL [DOT] COM               Google Scholar               GitHub

Selected Publications

Notes: Joint first authors are indicated using # and corresponding authors are indicated using *.

2024

• Learning Local and Global Temporal Contexts for Video Semantic Segmentation
  Guolei Sun, Yun Liu*, Henghui Ding, Min Wu, and Luc Van Gool
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
  [PDF] [Code] [Official Version]

• Rethinking Few-shot 3D Point Cloud Semantic Segmentation
  Zhaochong An, Guolei Sun*, Yun Liu*, Fayao Liu, Zongwei Wu, Dan Wang, Luc Van Gool, and Serge Belongie
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  [PDF] [Code] [Official Version]

• Vision Transformers with Hierarchical Attention
  (First titled "Transformer in Convolutional Neural Networks")
  Yun Liu, Yu-Huan Wu, Guolei Sun, Le Zhang, Ajad Chhatkuli, and Luc Van Gool
  Machine Intelligence Research (MIR), 2024
  [PDF] [Code] [Official Version]

• Rethinking Global Context in Crowd Counting
  (First titled "Boosting Crowd Counting with Transformers")
  Guolei Sun, Yun Liu*, Thomas Probst, Danda Pani Paudel, Nikola Popovic, and Luc Van Gool
  Machine Intelligence Research (MIR), 2024
  [PDF] [Official Version]

2023

• Revisiting Computer-Aided Tuberculosis Diagnosis
  Yun Liu, Yu-Huan Wu, Shi-Chen Zhang, Li Liu, Min Wu, and Ming-Ming Cheng
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
  [PDF] [Project Page] [Code] [Dataset on Google Drive] [Dataset on Baidu Yunpan] [中译版] [Online Challenge] [Official Version]

• Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution
  Ao Li, Le Zhang, Yun Liu, and Ce Zhu
  International Conference on Computer Vision (ICCV), 2023
  [PDF] [Supplementary Material] [Code] [Official Version]

• Indiscernible Object Counting in Underwater Scenes
  Guolei Sun, Zhaochong An, Yun Liu, Ce Liu, Christos Sakaridis, Deng-Ping Fan, and Luc Van Gool
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
  [PDF] [Supplementary Material] [Code] [Official Version]

• Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net
  Yu Qiu, Yun Liu*, Le Zhang, and Jing Xu*
  IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2023
  [PDF] [Code] [Official Version]

2022

• Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation
  Guolei Sun, Yun Liu*, Hao Tang, Ajad Chhatkuli, Le Zhang, and Luc Van Gool
  European Conference on Computer Vision (ECCV), 2022
  [PDF] [Code] [Official Version]

• Coarse-to-Fine Feature Mining for Video Semantic Segmentation
  Guolei Sun, Yun Liu*, Henghui Ding, Thomas Probst, and Luc Van Gool
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
  [PDF] [Code] [Official Version]

• P2T: Pyramid Pooling Transformer for Scene Understanding
  Yu-Huan Wu^#, Yun Liu^#, Xin Zhan, and Ming-Ming Cheng
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
  [PDF] [Code] [中译版] [Official Version]

• EDN: Salient Object Detection via Extremely-Downsampled Network
  Yu-Huan Wu^#, Yun Liu^#, Le Zhang, Ming-Ming Cheng, and Bo Ren
  IEEE Transactions on Image Processing (TIP), 2022
  [PDF] [Code] [Official Version]

• MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation
  Yu Qiu, Yun Liu*, Shijie Li, and Jing Xu*
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
  [PDF] [Code] [Official Version]

• A2SPPNet: Attentive Atrous Spatial Pyramid Pooling Network for Salient Object Detection
  Yu Qiu, Yun Liu*, Yanan Chen, Jianwen Zhang, Jinchao Zhu, and Jing Xu*
  IEEE Transactions on Multimedia (TMM), 2022
  [PDF] [Official Version]

• Zero Pixel Directional Boundary by Vector Transform
  Edoardo Mello Rella, Ajad Chhatkuli, Yun Liu, Ender Konukoglu, and Luc Van Gool
  International Conference on Learning Representations (ICLR), 2022
  [PDF] [Official Version]

2021

• Semantic Edge Detection with Diverse Deep Supervision
  Yun Liu, Ming-Ming Cheng, Deng-Ping Fan, Le Zhang, Jia-Wang Bian, and Dacheng Tao
  International Journal of Computer Vision (IJCV), 2021
  [PDF] [Code] [中译版] [Official Version]

• SAMNet: Stereoscopically Attentive Multi-scale Network for Lightweight Salient Object Detection
  Yun Liu^#, Xin-Yu Zhang^#, Jia-Wang Bian, Le Zhang, and Ming-Ming Cheng
  IEEE Transactions on Image Processing (TIP), 2021
  [PDF] [Code] [中译版] [Official Version]

• DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection
  Yun Liu, Ming-Ming Cheng, Xin-Yu Zhang, Guang-Yu Nie, and Meng Wang
  IEEE Transactions on Cybernetics (TCYB), 2021
  [PDF] [Saliency Maps] [中译版] [Official Version]

• MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
  Yu Qiu, Yun Liu*, Shijie Li, and Jing Xu*
  AAAI Conference on Artificial Intelligence (AAAI), 2021
  [PDF] [Code] [Official Version]

• MobileSal: Extremely Efficient RGB-D Salient Object Detection
  Yu-Huan Wu, Yun Liu, Jun Xu, Jia-Wang Bian, Yuchao Gu, and Ming-Ming Cheng
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
  [PDF] [Project Page] [Code] [Official Version]

• DOTS: Decoupling Operation and Topology in Differentiable Architecture Search
  Yuchao Gu, Lijuan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, and Ming-Ming Cheng
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
  [PDF] [Supplementary Material] [Code] [Official Version]

2020

• Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
  Yun Liu^#, Yu-Huan Wu^#, Peisong Wen, Yujun Shi, Yu Qiu, and Ming-Ming Cheng
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
  [PDF] [Code] [中译版] [Official Version]

• Rethinking Computer-aided Tuberculosis Diagnosis
  Yun Liu^#, Yu-Huan Wu^#, Yunfeng Ban, Huifang Wang, and Ming-Ming Cheng
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2020
  [PDF] [Project Page] [Dataset on Google Drive] [Dataset on Baidu Yunpan] [Online Challenge] [中译版] [Video] [PPT] [Official Version]

• Lightweight Salient Object Detection via Hierarchical Visual Perception Learning
  Yun Liu^#, Yu-Chao Gu^#, Xin-Yu Zhang^#, Weiwei Wang, and Ming-Ming Cheng
  IEEE Transactions on Cybernetics (TCYB), 2020
  [PDF] [Code] [中译版] [Official Version]

• MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
  Shijie Li, Yazan Abu Farha, Yun Liu, Ming-Ming Cheng, and Juergen Gall
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
  [PDF] [Project Page] [Code] [Official Version]

• Ordered or Orderless: A Revisit for Video based Person Re-Identification
  Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, and Chunhua Shen
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
  [PDF] [Code] [Official Version]

• GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence
  Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, and Ian Reid
  International Journal of Computer Vision (IJCV), 2020
  [PDF] [Project Page] [Code] [Video] [Official Version]

• Pyramid Constrained Self-Attention Network for Fast Video Salient Object Detection
  Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu
  AAAI Conference on Artificial Intelligence (AAAI), 2020
  [PDF] [Project Page] [Code] [Official Version]

• RefinedBox: Refining for Fewer and High-quality Object Proposals
  Yun Liu, Shi-Jie Li, and Ming-Ming Cheng
  Neurocomputing, 2020
  [PDF] [Code] [中译版] [Official Version]

2019

• Richer Convolutional Features for Edge Detection
  Yun Liu, Ming-Ming Cheng, Xiaowei Hu, Jia-Wang Bian, Le Zhang, Xiang Bai, and Jinhui Tang
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
  [PDF] [Project Page] [Code] [Official Version]

• Nonlinear Regression via Deep Negative Correlation Learning
  Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, and Zeng Zeng
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
  [PDF] [Project Page] [Code] [Official Version]

• Scoot: A Perceptual Metric for Facial Sketches
  Deng-Ping Fan, ShengChuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Paul Rosin, and Rongrong Ji
  International Conference on Computer Vision (ICCV), 2019
  [PDF] [Project Page] [Supplementary Material] [Code] [Official Version]

• Multi-Level Context Ultra-Aggregation for Stereo Matching
  Guang-Yu Nie, Ming-Ming Cheng, Yun Liu, Zhengfa Liang, Deng-Ping Fan, Yue Liu, and Yongtian Wang
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
  [PDF] [Project Page] [Supplementary Material] [PPT] [Official Version]

• BING: Binarized Normed Gradients for Objectness Estimation at 300fps
  Ming-Ming Cheng^#, Yun Liu^#, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, and Philip Torr
  Computational Visual Media (CVMJ), 2019
  [PDF] [Project Page] [Code] [Official Version]

2016 - 2018

• DEL: Deep Embedding Learning for Efficient Image Segmentation
  Yun Liu, Peng-Tao Jiang, Vahan Petrosyan, Shi-Jie Li, Jiawang Bian, Le Zhang, and Ming-Ming Cheng
  International Joint Conference on Artificial Intelligence (IJCAI), 2018
  [PDF] [Project Page] [Code] [中译版] [Official Version]

• Crowd Counting with Deep Negative Correlation Learning
  Zenglin Shi, Le Zhang, Yun Liu, XiaoFeng Cao, Yangdong Ye, Ming-Ming Cheng, and Guoyan Zheng
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
  [PDF] [Project Page] [Code] [Official Version]

• Sequential Optimization for Efficient High-Quality Object Proposal Generation
  Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, and Philip H.S. Torr
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017
  [PDF] [Code] [Official Version]

• Structure-measure: A New Way to Evaluate Foreground Maps
  DengPing Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji
  International Conference on Computer Vision (ICCV, Spotlight), 2017
  [PDF] [Project Page] [Code] [PPT] [Official Version]

• Richer Convolutional Features for Edge Detection
  Yun Liu, Ming-Ming Cheng, Xiaowei Hu, Kai Wang, and Xiang Bai
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
  [PDF] [Project Page] [Code] [中译版] [Official Version]

• HFS: Hierarchical Feature Selection for Efficient Image Segmentation
  Ming-Ming Cheng^#, Yun Liu^#, Qibin Hou, Jiawang Bian, Philip Torr, Shi-Min Hu, and Zhuowen Tu
  European Conference on Computer Vision (ECCV), 2016
  [PDF] [Project Page] [Code] [中译版] [Official Version]