DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
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- No authorship claim recorded yet. The source records list arXiv, Crossref, Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy and A. Yuille.
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- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs arXiv · published 2016-06-02 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 abed22b62d95ac03fb0155d400489367e266548ecad199cf3f04b91a8ecaaa0f). Submission history as arXiv records it (UTC): v1 2 June 2016; v2 12 May 2017. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Crossref · published 2018 · Crossref record of the journal version: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-848 (DOI 10.1109/tpami.2017.2699184), retrieved for the candidate pack on 2026-10-28. The date is the publisher-deposited date, kept at year precision; it was not checked against the issue.
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs arXiv · published 2016-06-02 · arXiv:1606.00915v1, submitted 2 June 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 0554ad33505ab4067960b692d6d07c05fca2495ef919f1287215f7fab6619724). The author list is the byline as printed on the first page.
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- In its own wordsThe paper addresses the task of semantic image segmentation with deep learning and states that it makes three main contributions.“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.” arXiv v1, PDF p. 1, abstract2016-06-02 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own wordsThe paper proposes atrous spatial pyramid pooling (ASPP) for segmenting objects at multiple scales.“Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales.” arXiv v1, PDF p. 1, abstract2016-06-02 · publication · Stated in source · Supported Inspect claim & evidence ↗