{"url":"/method/deeplabv2","slug":"deeplabv2","name":"DeepLabv2","full_name":"DeepLabv2","full_name_withheld":false,"description_markdown":"**DeepLabv2** is an architecture for semantic segmentation that build on [DeepLab](https://paperswithcode.com/method/deeplab) with an atrous [spatial pyramid pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) scheme. Here we have parallel dilated convolutions with different rates applied in the input feature map, which are then fused together. As objects of the same class can have different sizes in the image, [ASPP](https://paperswithcode.com/method/aspp) helps to account for different object sizes.","description_state":"present","introduced_year":null,"introduced_by":{"title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","paper":"/paper/deeplab-semantic-image-segmentation-with-deep","first_author":"Liang-Chieh Chen","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/deeplab-semantic-image-segmentation-with-deep"},"source":{"url":"http://arxiv.org/abs/1606.00915v2","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/tensorflow/models/tree/master/research/deeplab","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Semantic Segmentation Models","url":"/methods/category/semantic-segmentation-models","pwc_aliases":["segmentation-models"]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":null,"title":"Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation","date":"2024-04-02","arxiv_id":"2404.02065","n_code_links":0,"syntology":null},{"paper":"/paper/threshold-adaptive-unsupervised-focal-loss","title":"Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation","date":"2022-08-23","arxiv_id":"2208.10716","n_code_links":1,"syntology":null},{"paper":"/paper/fda-fourier-domain-adaptation-for-semantic","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","date":"2020-04-11","arxiv_id":"2004.05498","n_code_links":3,"syntology":{"ran":2,"of":3,"unverified":1,"pointer_only":3}},{"paper":"/paper/deeplab-semantic-image-segmentation-with-deep","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","date":"2016-06-02","arxiv_id":"1606.00915","n_code_links":47,"syntology":{"ran":28,"of":63,"unverified":35,"pointer_only":16}}],"papers_shown":4,"tasks":[{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":4},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/unsupervised-domain-adaptation","name":"Unsupervised Domain Adaptation","papers":2},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":1},{"task":"/task/semi-supervised-semantic-segmentation","name":"Semi-Supervised Semantic Segmentation","papers":1}],"tasks_shown":7,"n_tasks":7,"usage_by_year":[{"year":"2016","papers":1},{"year":"2020","papers":1},{"year":"2022","papers":1},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/deeplabv2"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}