G
George Papandreou
Researcher at Google
Publications - 57
Citations - 47608
George Papandreou is an academic researcher from Google. The author has contributed to research in topics: Segmentation & Convolutional neural network. The author has an hindex of 32, co-authored 56 publications receiving 31881 citations. Previous affiliations of George Papandreou include Toyota & National and Kapodistrian University of Athens.
Papers
More filters
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.
Posted Content
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.
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.
Posted Content
Rethinking Atrous Convolution for Semantic Image Segmentation
TL;DR: The proposed `DeepLabv3' system significantly improves over the previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.
Posted Content
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TL;DR: This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF).