L
Liang-Chieh Chen
Researcher at Google
Publications - 90
Citations - 76599
Liang-Chieh Chen is an academic researcher from Google. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 43, co-authored 77 publications receiving 45955 citations. Previous affiliations of Liang-Chieh Chen include University of California & National Taiwan University.
Papers
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Journal ArticleDOI
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: DeepLab as discussed by the authors proposes atrous spatial pyramid pooling (ASPP) to segment objects at multiple scales by probing an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views.
Proceedings ArticleDOI
MobileNetV2: Inverted Residuals and Linear Bottlenecks
TL;DR: MobileNetV2 as mentioned in this paper is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers and intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity.
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
TL;DR: A new mobile architecture, MobileNetV2, is described that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes and allows decoupling of the input/output domains from the expressiveness of the transformation.
Book ChapterDOI
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
TL;DR: This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.