Xiangyu YueEmail: xyyue [at] ie [dot] cuhk [dot] edu [dot] hk |
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I am currently an Assistant Professor in the Department of Information Engineering at the Chinese University of Hong Kong. Before joining Multimedia Lab,
I received my PhD from Electrical Engineering and Computer Science at University of California, Berkeley, working with Prof. Alberto Sangiovanni Vincentelli and Prof. Kurt Keutzer at Berkeley AI Research. I am broadly interested in various areas in machine learning, especially building systems that generalize well, require less data and computing resources, and are interpretable.
Prior to Berkeley, I received my M.S. degree from Stanford University and B.S. degree from Nanjing University. I did internships at Google Research, Google [x] Robotics, Baidu AI Research, and Tencent AI Lab. I have received the Lotfi A. Zadeh Prize for my research work. *NEW* I have multiple fully-funded PhD, MPhil, postdoc, RA, and intern positions available. Feel free to Email me if you are interested. Google Scholar | LinkedIn | Twitter | DBLP |
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Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
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MLSeg: Image and Video Segmentation as Multi-Label Classification and Selected-Label Pixel Classification
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Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
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Self-Supervised Pretraining Improves Self-Supervised Pretraining
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Multi-source Few-shot Domain Adaptation
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Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion
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On Ensemble Methods for Long-Tailed Recognition
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AugPrune: Robust Network Pruning via Augmented Data
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Scene-aware Learning Network for Radar Object Detection
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Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
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Curriculum Cyclegan for Textual Sentiment Domain Adaptation with Multiple Sources
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Emotional Semantics-preserved and Feature-aligned CycleGAN for Visual Emotion Adaptation
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A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
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PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
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Scenic: a Language for Scenario Specification and Scene Generation
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Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
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Multi-source Domain Adaptation for Semantic Segmentation
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Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud
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Counterexample-guided Data Augmentation
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Shift: A Zero-flop, Zero-parameter Alternative to Apatial Convolutions
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A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
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Formal Specification for Deep Neural Networks
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SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-time Road-object Segmentation from 3d LiDAR Point Cloud
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