{"project":[{"call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036"}],"year":"2016","publist_id":"5842","volume":9908,"intvolume":"      9908","language":[{"iso":"eng"}],"_id":"1369","ec_funded":1,"conference":{"end_date":"2016-10-14","location":"Amsterdam, The Netherlands","name":"ECCV: European Conference on Computer Vision","start_date":"2016-10-11"},"citation":{"ieee":"A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.","mla":"Kolesnikov, Alexander, and Christoph Lampert. <i>Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation</i>. Vol. 9908, Springer, 2016, pp. 695–711, doi:<a href=\"https://doi.org/10.1007/978-3-319-46493-0_42\">10.1007/978-3-319-46493-0_42</a>.","short":"A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.","apa":"Kolesnikov, A., &#38; Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer. <a href=\"https://doi.org/10.1007/978-3-319-46493-0_42\">https://doi.org/10.1007/978-3-319-46493-0_42</a>","ista":"Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. ECCV: European Conference on Computer Vision, LNCS, vol. 9908, 695–711.","chicago":"Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation,” 9908:695–711. Springer, 2016. <a href=\"https://doi.org/10.1007/978-3-319-46493-0_42\">https://doi.org/10.1007/978-3-319-46493-0_42</a>.","ama":"Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:<a href=\"https://doi.org/10.1007/978-3-319-46493-0_42\">10.1007/978-3-319-46493-0_42</a>"},"doi":"10.1007/978-3-319-46493-0_42","page":"695 - 711","author":[{"last_name":"Kolesnikov","first_name":"Alexander","full_name":"Kolesnikov, Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887"}],"alternative_title":["LNCS"],"status":"public","title":"Seed, expand and constrain: Three principles for weakly-supervised image segmentation","quality_controlled":"1","scopus_import":1,"publisher":"Springer","date_created":"2018-12-11T11:51:37Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa":1,"oa_version":"Preprint","abstract":[{"lang":"eng","text":"We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations."}],"main_file_link":[{"url":"https://arxiv.org/abs/1603.06098","open_access":"1"}],"department":[{"_id":"ChLa"}],"publication_status":"published","date_published":"2016-09-15T00:00:00Z","date_updated":"2021-01-12T06:50:12Z","month":"09","day":"15","type":"conference"}