His scientific interests lie mostly in Artificial intelligence, Machine learning, Computer vision, Pattern recognition and Benchmark. His study in Data mining extends to Artificial intelligence with its themes. His Machine learning research incorporates elements of Robust statistics and Content-based image retrieval.
In general Computer vision study, his work on Channel, Color mapping, Color image and Image processing often relates to the realm of Disjoint sets, thereby connecting several areas of interest. His Pattern recognition study combines topics from a wide range of disciplines, such as Point cloud and Representation. His work is dedicated to discovering how Feature extraction, Solver are connected with Object and other disciplines.
Cewu Lu mainly focuses on Artificial intelligence, Object, Machine learning, Computer vision and Code. Artificial intelligence is often connected to Pattern recognition in his work. His biological study spans a wide range of topics, including Semantics, Theoretical computer science and Human–computer interaction.
His studies in Computer vision integrate themes in fields like Robot and Robustness. His work carried out in the field of Benchmark brings together such families of science as Image and Real-time computing. His research in Object detection intersects with topics in Feature extraction, Pascal and Convolutional neural network.
Artificial intelligence, Object, Code, Computer vision and Machine learning are his primary areas of study. Cewu Lu performs multidisciplinary study on Artificial intelligence and GRASP in his works. His study in Object is interdisciplinary in nature, drawing from both Theoretical computer science, Aggregate and Graphics.
Machine learning and Modality are frequently intertwined in his study. His Deep learning research is multidisciplinary, relying on both Object detection, Information redundancy, Reeb graph and Algorithm. His Pose research includes themes of RGB color model and Monocular.
Cewu Lu mainly investigates Artificial intelligence, Object, Machine learning, Robustness and Computer vision. His research on Artificial intelligence frequently connects to adjacent areas such as Task analysis. Cewu Lu works mostly in the field of Object, limiting it down to topics relating to Graphics and, in certain cases, Data mining, as a part of the same area of interest.
His research in the fields of Leverage overlaps with other disciplines such as Interaction information. He interconnects Depth map, Polygon mesh and Silhouette in the investigation of issues within Robustness. In his work, Feature extraction is strongly intertwined with Supervised learning, which is a subfield of Semantics.
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Image smoothing via L0 gradient minimization
Li Xu;Cewu Lu;Yi Xu;Jiaya Jia.
international conference on computer graphics and interactive techniques (2011)
RMPE: Regional Multi-person Pose Estimation
Hao-Shu Fang;Shuqin Xie;Yu-Wing Tai;Cewu Lu.
international conference on computer vision (2017)
Abnormal Event Detection at 150 FPS in MATLAB
Cewu Lu;Jianping Shi;Jiaya Jia.
international conference on computer vision (2013)
Visual Relationship Detection with Language Priors
Cewu Lu;Ranjay Krishna;Michael S. Bernstein;Li Fei-Fei.
european conference on computer vision (2016)
A scalable active framework for region annotation in 3D shape collections
Li Yi;Vladimir G. Kim;Duygu Ceylan;I-Chao Shen.
international conference on computer graphics and interactive techniques (2016)
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Chen Wang;Danfei Xu;Yuke Zhu;Roberto Martin-Martin.
computer vision and pattern recognition (2019)
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
Di Lin;Xiaoyong Shen;Cewu Lu;Jiaya Jia.
computer vision and pattern recognition (2015)
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation.
Mingyang Jiang;Yiran Wu;Cewu Lu.
arXiv: Computer Vision and Pattern Recognition (2018)
Two-Class Weather Classification
Cewu Lu;Di Lin;Jiaya Jia;Chi-Keung Tang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Virtual to Real Reinforcement Learning for Autonomous Driving.
Xinlei Pan;Yurong You;Ziyan Wang;Cewu Lu.
british machine vision conference (2017)
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