His main research concerns Artificial intelligence, Pattern recognition, Segmentation, Pixel and Convolutional neural network. His research related to Parsing and Point cloud might be considered part of Artificial intelligence. Peng Wang has included themes like Pascal and Benchmark in his Pattern recognition study.
Peng Wang specializes in Segmentation, namely Image segmentation. His Pixel study is concerned with the larger field of Computer vision. His Convolutional neural network study incorporates themes from Inference and Conditional random field.
His primary areas of investigation include Artificial intelligence, Pixel, Pattern recognition, Computer vision and Segmentation. Convolutional neural network, Object, Image, Image segmentation and Pascal are the primary areas of interest in his Artificial intelligence study. His Pixel study combines topics in areas such as Depth map, Embedding, Geometry, Normal and Algorithm.
Peng Wang interconnects Inference and Parsing in the investigation of issues within Pattern recognition. His work in Computer vision addresses subjects such as Deep learning, which are connected to disciplines such as Variation. His studies in Segmentation integrate themes in fields like Optical flow, Pose, Robustness and Conditional random field.
Peng Wang focuses on Artificial intelligence, Pixel, Computer vision, Depth map and Convolutional neural network. His biological study spans a wide range of topics, including Algorithm and Kernel. His Computer vision research is multidisciplinary, relying on both Translation and Benchmark.
Peng Wang has researched Depth map in several fields, including Single image and Pattern recognition. His work carried out in the field of Convolutional neural network brings together such families of science as Feature learning and Feature. His Segmentation research includes elements of Optical flow and Point cloud.
The scientist’s investigation covers issues in Artificial intelligence, Pixel, Convolutional neural network, Computer vision and Segmentation. Particularly relevant to Image segmentation is his body of work in Artificial intelligence. His Pixel research incorporates themes from Depth map, Algorithm and Pyramid.
His Convolutional neural network research integrates issues from Translation, Rotation and Benchmark. In general Computer vision, his work in Optical flow and Motion is often linked to Scale and Animation linking many areas of study. His work deals with themes such as Point cloud and Sensor fusion, which intersect with Segmentation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Towards unified depth and semantic prediction from a single image
Peng Wang;Xiaohui Shen;Zhe Lin;Scott Cohen.
computer vision and pattern recognition (2015)
The ApolloScape Dataset for Autonomous Driving
Xinyu Huang;Xinjing Cheng;Qichuan Geng;Binbin Cao.
Semantic Instance Segmentation via Deep Metric Learning
Alireza Fathi;Zbigniew Wojna;Vivek Rathod;Peng Wang.
arXiv: Computer Vision and Pattern Recognition (2017)
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
Liang-Chieh Chen;Alexander Hermans;George Papandreou;Florian Schroff.
computer vision and pattern recognition (2018)
Occlusion Aware Unsupervised Learning of Optical Flow
Yang Wang;Yi Yang;Zhenheng Yang;Liang Zhao.
computer vision and pattern recognition (2018)
Structure-Sensitive Superpixels via Geodesic Distance
Peng Wang;Gang Zeng;Rui Gan;Jingdong Wang.
International Journal of Computer Vision (2013)
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
Xinjing Cheng;Peng Wang;Ruigang Yang.
european conference on computer vision (2018)
The ApolloScape Open Dataset for Autonomous Driving and Its Application
Xinyu Huang;Peng Wang;Xinjing Cheng;Dingfu Zhou.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
Joint Multi-person Pose Estimation and Semantic Part Segmentation
Fangting Xia;Peng Wang;Xianjie Chen;Alan L. Yuille.
computer vision and pattern recognition (2017)
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
Fangting Xia;Peng Wang;Liang-Chieh Chen;Alan L. Yuille.
european conference on computer vision (2016)
Profile was last updated on December 6th, 2021.
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The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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