Xiangyu YueEmail: xyyue [at] ie.cuhk.edu.hk |
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I am currently an Assistant Professor in the Department of Information Engineering at Chinese University of Hong Kong, with the Multimedia Lab (MMLab).
I received my Ph.D. 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 including but not limited to: multi-modal learning, generative models, multi-modal LLMs, transfer learning, domain adaptation, interpretable systems, etc.
Prior to Berkeley, I received my M.S. degree from Stanford University and B.S. degree from Nanjing University. I have spent time at Google Research, Google [x] Robotics, Baidu AI Research, and Tencent AI Lab. I received the prestigious Lotfi A. Zadeh Award for my research work. *NEW* I have multipple fully-funded Ph.D. / Mphil (2025, 2026) positions all year round. We have postdoc, RA, visiting student and intern positions available as well. Feel free to Email me if you are interested. (Please also highlight if you have other funding sources or support.) Google Scholar | LinkedIn | Twitter | DBLP |
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OneLLM: One Framework to Align All Modalities with Language
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Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
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UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
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Meta-Transformer: A Unified Framework for Multimodal Learning
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LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
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Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation
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Beating Backdoor Attack at Its Own Game
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Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models
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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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