Liang-Chieh Chen mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, Image segmentation and Segmentation. Liang-Chieh Chen combines subjects such as Pyramid, Pascal and Test set with his study of Pattern recognition. Liang-Chieh Chen works mostly in the field of Pyramid, limiting it down to topics relating to Convolution and, in certain cases, Deep learning and Upsampling.
The Convolutional neural network study combines topics in areas such as Semantic image segmentation, CRFS, Conditional random field, Scale-space segmentation and Graphical model. His Image segmentation research focuses on subjects like Object detection, which are linked to Algorithm and Artificial neural network. His Segmentation research focuses on subjects like Pooling, which are linked to Separable space, Mobile phone and Search algorithm.
Liang-Chieh Chen focuses on Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Image segmentation. His Artificial intelligence study frequently draws connections between related disciplines such as Machine learning. His Segmentation study combines topics in areas such as Object, Object detection, Contextual image classification, Algorithm and Discriminative model.
His Pattern recognition research incorporates themes from Pixel and Parsing. Liang-Chieh Chen studied Computer vision and Embedding that intersect with Probabilistic logic and Task. His work carried out in the field of Convolutional neural network brings together such families of science as Convolution and Scale-space segmentation.
Segmentation, Artificial intelligence, Computer vision, Test set and Image segmentation are his primary areas of study. His study in Segmentation is interdisciplinary in nature, drawing from both Closing, Algorithm and Convolution. His study connects Pattern recognition and Artificial intelligence.
His Pattern recognition study incorporates themes from Image resolution, Frame rate, Margin and Image prediction. The study incorporates disciplines such as Embedding, Probabilistic logic, Metric and Benchmark in addition to Computer vision. In his study, which falls under the umbrella issue of Test set, Leverage is strongly linked to Discriminative model.
Liang-Chieh Chen mostly deals with Segmentation, Algorithm, Convolution, Artificial intelligence and Range. His biological study spans a wide range of topics, including Image resolution and Pyramid. His studies deal with areas such as Image, Image segmentation, Margin, Frame rate and Feature extraction as well as Image resolution.
His Image segmentation study improves the overall literature in Pattern recognition. His Pyramid research integrates issues from Object, Computer vision and Pyramid. His studies in Range integrate themes in fields like Contextual image classification and Block.
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.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen;George Papandreou;Iasonas Kokkinos;Kevin Murphy.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler;Andrew Howard;Menglong Zhu;Andrey Zhmoginov.
computer vision and pattern recognition (2018)
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen;Yukun Zhu;George Papandreou;Florian Schroff.
european conference on computer vision (2018)
Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen;George Papandreou;Florian Schroff;Hartwig Adam.
arXiv: Computer Vision and Pattern Recognition (2017)
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Liang-Chieh Chen;George Papandreou;Iasonas Kokkinos;Kevin Murphy.
international conference on learning representations (2015)
Searching for MobileNetV3
Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le.
international conference on computer vision (2019)
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
George Papandreou;Liang-Chieh Chen;Kevin P. Murphy;Alan L. Yuille.
international conference on computer vision (2015)
Attention to Scale: Scale-Aware Semantic Image Segmentation
Liang-Chieh Chen;Yi Yang;Jiang Wang;Wei Xu.
computer vision and pattern recognition (2016)
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen;George Papandreou;Iasonas Kokkinos;Kevin Murphy.
arXiv: Computer Vision and Pattern Recognition (2016)
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu;Liang-Chieh Chen;Florian Schroff;Hartwig Adam.
computer vision and pattern recognition (2019)
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