2023 - Research.com Computer Science in China Leader Award
2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to machine learning, computer vision, and remote sensing
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Hash function and Theoretical computer science. Wei Liu studied Artificial intelligence and Computer vision that intersect with Resolution. The study incorporates disciplines such as Modal, Feature, Contextual image classification, Image and Supervised learning in addition to Pattern recognition.
His Machine learning research includes themes of Training set and Robustness. His Hash function research is multidisciplinary, incorporating elements of Hamming space, Deep learning and Search engine indexing. His Theoretical computer science research incorporates themes from Feature hashing, Nearest neighbor search, Universal hashing and Dynamic perfect hashing.
Wei Liu focuses on Artificial intelligence, Computer vision, Pattern recognition, Image and Machine learning. His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Natural language processing, with regards to Word. His study in the field of Tracking and Object also crosses realms of Field and Process.
Wei Liu does research in Pattern recognition, focusing on Feature extraction specifically. His Machine learning research integrates issues from Hash function, Image retrieval, Metric and Robustness. His research in Double hashing, Universal hashing, Dynamic perfect hashing and Feature hashing are components of Hash function.
His primary scientific interests are in Artificial intelligence, Computer vision, Image, Deep learning and Machine learning. In his research on the topic of Artificial intelligence, Similarity is strongly related with Pattern recognition. His work on Augmented reality, Face, Object and Tracking as part of general Computer vision research is frequently linked to Process, bridging the gap between disciplines.
The Image study which covers Sample that intersects with Function. Wei Liu studies Machine learning, focusing on Discriminative model in particular. Wei Liu interconnects Language model, Natural language, Natural language processing and Metric in the investigation of issues within Closed captioning.
Wei Liu mainly focuses on Artificial intelligence, Algorithm, Deep learning, Pattern recognition and Applied mathematics. The Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His Algorithm study integrates concerns from other disciplines, such as Shearing, Filter bank, Image fusion and Spatial frequency.
His Deep learning research is multidisciplinary, relying on both Object detection, Minimum bounding box, Inference, Cluster analysis and Point. Wei Liu studies Pattern recognition, namely Convolutional neural network. The various areas that Wei Liu examines in his Applied mathematics study include Gradient descent, Convex function and Smoothness.
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Supervised hashing with kernels
Wei Liu;Jun Wang;Rongrong Ji;Yu-Gang Jiang.
computer vision and pattern recognition (2012)
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
Long Chen;Hanwang Zhang;Jun Xiao;Liqiang Nie.
computer vision and pattern recognition (2017)
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Hao Wang;Yitong Wang;Zheng Zhou;Xing Ji.
computer vision and pattern recognition (2018)
Hashing with Graphs
Wei Liu;Jun Wang;Sanjiv Kumar;Shih-fu Chang.
international conference on machine learning (2011)
Supervised Discrete Hashing
Fumin Shen;Chunhua Shen;Wei Liu;Heng Tao Shen.
computer vision and pattern recognition (2015)
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zhaohui Zheng;Ping Wang;Wei Liu;Jinze Li.
national conference on artificial intelligence (2020)
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Nanyang Wang;Yinda Zhang;Zhuwen Li;Yanwei Fu.
european conference on computer vision (2018)
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention
Jingyuan Chen;Hanwang Zhang;Xiangnan He;Liqiang Nie.
international acm sigir conference on research and development in information retrieval (2017)
Large Graph Construction for Scalable Semi-Supervised Learning
Wei Liu;Junfeng He;Shih-fu Chang.
international conference on machine learning (2010)
Multiple object tracking: A literature review
Wenhan Luo;Wenhan Luo;Junliang Xing;Anton Milan;Xiaoqin Zhang.
Artificial Intelligence (2021)
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