DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
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chen, 2015, Semantic image segmentation with deep convolutional nets and fully connected CRFs, Proc Int Conf Learn Representations
everingham, 2014, The PASCAL visual object classes challenge a retrospective, Int J Comput Vis
lempitsky, 2011, Pylon model for semantic segmentation, Proc Advances Neural Inf Process Syst, 1485
he, 2014, Spatial pyramid pooling in deep convolutional networks for visual recognition, Proc Eur Conf Comput Vis, 346
krähenbühl, 2011, Efficient inference in fully connected CRFs with Gaussian edge potentials, Proc Advances Neural Inf Process Syst, 109
he, 2004, Multiscale conditional random fields for image labeling, Proc IEEE Comput Soc Conf Comput Vis Pattern Recog, 695
badrinarayanan, 2015, Segnet: A deep convolutional encoder-decoder architecture for image segmentation
dai, 2014, Convolutional feature masking for joint object and stuff segmentation
papandreou, 2015, Weakly- and semi-supervised learning of a DCNN for semantic image segmentation, Proc IEEE Int Conf Comput Vis, 1742
bell, 2014, Material recognition in the wild with the materials in context database
cogswell, 2014, Combining the best of graphical models and convnets for semantic segmentation
eigen, 2014, Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
lin, 2015, Efficient piecewise training of deep structured models for semantic segmentation
simonyan, 2015, Very deep convolutional networks for large-scale image recognition, Proc Int Conf Learn Representations
sermanet, 2013, OverFeat: Integrated recognition, localization and detection using convolutional networks
szegedy, 2014, Going deeper with convolutions
hariharan, 2014, Simultaneous detection and segmentation, Proc Eur Conf Comput Vis, 297
carreira, 2012, Semantic segmentation with second-order pooling, Proc Eur Conf Comput Vis, 430
tu, 2010, Auto-context and its application to high-level vision tasks and 3D brain image segmentation, IEEE Trans Pattern Anal Mach Intell, 32, 1744, 10.1109/TPAMI.2009.186
liang, 2015, Proposal-free network for instance-level object segmentation
hong, 2015, Decoupled deep neural network for semi-supervised semantic segmentation, Proc 28th Int Conf Neural Inf Process Syst, 1495
yu, 2016, Multi-scale context aggregation by dilated convolutions, Proc Int Conf Learn Representations
dai, 2016, R-FCN: Object detection via region-based fully convolutional networks
chen, 2015, ABC-CNN: An attention based convolutional neural network for visual question answering
chen, 2015, Learning deep structured models, Proc Int Conf Int Conf Mach Learn, 1785
schwing, 2015, Fully connected deep structured networks
krizhevsky, 2013, ImageNet classification with deep convolutional neural networks, Proc 25th Int Conf Neural Inf Process Syst, 1097
pinheiro, 2014, Weakly supervised semantic segmentation with convolutional networks
xia, 2015, Zoom better to see clearer: Huamn part segmentation with auto zoom net
wu, 2016, Bridging category-level and instance-level semantic image segmentation
shen, 2016, Fast semantic image segmentation with high order context and guided filtering
arnab, 2015, Higher order potentials in end-to-end trainable conditional random fields
ghiasi, 2016, Laplacian reconstruction and refinement for semantic segmentation, 10.1007/978-3-319-46487-9_32
ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, Proc Int Conf Medical Image Comput Comput -Assisted Intervention, 234
liang, 2015, Semantic object parsing with local-global long short-term memory
ren, 2015, Faster R-CNN: Towards real-time object detection with region proposal networks, Proc 28th Int Conf Neural Inf Process Syst, 91
he, 2015, Deep residual learning for image recognition
liu, 2015, SSD: Single shot multibox detector
zeiler, 2014, Visualizing and understanding convolutional networks, Proc Eur Conf Comput Vis, 818
wu, 2016, High-performance semantic segmentation using very deep fully convolutional networks
kokkinos, 2016, Pushing the boundaries of boundary detection using deep learning, Proc Int Conf Learn Representations
abadi, 2016, Tensorflow: Large-scale machine learning on heterogeneous distributed systems
yan, 2016, Combining the best of convolutional layers and recurrent layers: A hybrid network for semantic segmentation
liu, 2015, Parsenet: Looking wider to see better
lin, 2014, Microsoft COCO: Common objects in context, Proc Eur Conf Comput Vis, 740