His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Object detection and Structure. Visualization, Feature extraction, Categorization, Discriminative model and Parsing are among the areas of Artificial intelligence where he concentrates his study. His study in Discriminative model is interdisciplinary in nature, drawing from both Artificial neural network, Supervised learning and Training set.
His biological study spans a wide range of topics, including Contextual image classification, Facial recognition system, Generative model and RGB color model. His work deals with themes such as Viola–Jones object detection framework and Closed captioning, which intersect with Machine learning. His Object detection research is multidisciplinary, incorporating elements of Pyramid, Segmentation, Software engineering and Pyramid.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Object detection. Dahua Lin works mostly in the field of Artificial intelligence, limiting it down to topics relating to Natural language processing and, in certain cases, Closed captioning. His work in the fields of Pattern recognition, such as Discriminative model and Feature extraction, overlaps with other areas such as Structure.
His research in the fields of Feature and Deep learning overlaps with other disciplines such as Sampling and Generalization. Dahua Lin combines subjects such as Representation and Leverage with his study of Computer vision. His study looks at the intersection of Segmentation and topics like Inpainting with Upsampling.
Dahua Lin spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Feature and Machine learning. All of his Artificial intelligence and Object detection, Image, Parsing, Pyramid and Leverage investigations are sub-components of the entire Artificial intelligence study. His work is dedicated to discovering how Object detection, Pyramid are connected with Noise and other disciplines.
His work on Object, Monocular and Point cloud as part of general Computer vision research is frequently linked to Electronic equipment and Storytelling, bridging the gap between disciplines. His Pattern recognition research integrates issues from Regularization and Graph. His studies deal with areas such as Domain and Outlier as well as Machine learning.
Dahua Lin mostly deals with Artificial intelligence, Computer vision, Machine learning, Pattern recognition and Object detection. Dahua Lin has included themes like Task analysis and Natural language processing in his Artificial intelligence study. The various areas that Dahua Lin examines in his Computer vision study include Binaural recording and Representation.
Dahua Lin merges Machine learning with Generalization in his study. His Pattern recognition research incorporates themes from Pyramid and Regularization. His Object detection research is multidisciplinary, relying on both Minimum bounding box and Task.
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Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Limin Wang;Yuanjun Xiong;Zhe Wang;Yu Qiao.
european conference on computer vision (2016)
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Sijie Yan;Yuanjun Xiong;Dahua Lin.
national conference on artificial intelligence (2018)
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Zhirong Wu;Yuanjun Xiong;Stella X. Yu;Dahua Lin.
computer vision and pattern recognition (2018)
MMDetection: Open MMLab Detection Toolbox and Benchmark.
Kai Chen;Jiaqi Wang;Jiangmiao Pang;Yuhang Cao.
arXiv: Computer Vision and Pattern Recognition (2019)
PSANet: Point-wise Spatial Attention Network for Scene Parsing
Hengshuang Zhao;Yi Zhang;Shu Liu;Jianping Shi.
european conference on computer vision (2018)
Temporal Action Detection with Structured Segment Networks
Yue Zhao;Yuanjun Xiong;Yuanjun Xiong;Limin Wang;Zhirong Wu;Zhirong Wu.
international conference on computer vision (2017)
Hybrid Task Cascade for Instance Segmentation
Kai Chen;Wanli Ouyang;Chen Change Loy;Dahua Lin.
computer vision and pattern recognition (2019)
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras
Dahua Lin;Sanja Fidler;Raquel Urtasun.
international conference on computer vision (2013)
Libra R-CNN: Towards Balanced Learning for Object Detection
Jiangmiao Pang;Kai Chen;Jianping Shi;Huajun Feng.
computer vision and pattern recognition (2019)
Detecting Visual Relationships with Deep Relational Networks
Bo Dai;Yuqi Zhang;Dahua Lin.
computer vision and pattern recognition (2017)
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