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DeepLab: SemanticImage Segmentationwith DeepConvolutional Nets,Atrous Convolution,and Fully ConnectedCRFs Liang-Chieh ChenGeorge PapandreouIasonas Kokkinos2017 год

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
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Аннотация: In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.
Год издания: 2017
Авторы: Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan Yuille
Издательство: IEEE Computer Society
Источник: IEEE Transactions on Pattern Analysis and Machine Intelligence
Ключевые слова: Advanced Neural Network Applications, Advanced Image and Video Retrieval Techniques, Robotics and Sensor-Based Localization
Другие ссылки: IEEE Transactions on Pattern Analysis and Machine Intelligence (HTML)
UCL Discovery (University College London) (PDF)
UCL Discovery (University College London) (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
UCL Discovery (University College London) (HTML)
PubMed (HTML)