{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-extreme-cut-from-extreme-points-to","title":"Deep Extreme Cut: From Extreme Points to Object Segmentation","arxiv_id":"1711.09081","date":"2017-11-24","proceeding":"CVPR 2018 6","authors":["Kevis-Kokitsi Maninis","Sergi Caelles","Jordi Pont-Tuset","Luc van Gool"],"abstract":"This paper explores the use of extreme points in an object (left-most,\nright-most, top, bottom pixels) as input to obtain precise object segmentation\nfor images and videos. We do so by adding an extra channel to the image in the\ninput of a convolutional neural network (CNN), which contains a Gaussian\ncentered in each of the extreme points. The CNN learns to transform this\ninformation into a segmentation of an object that matches those extreme points.\nWe demonstrate the usefulness of this approach for guided segmentation\n(grabcut-style), interactive segmentation, video object segmentation, and dense\nsegmentation annotation. We show that we obtain the most precise results to\ndate, also with less user input, in an extensive and varied selection of\nbenchmarks and datasets. All our models and code are publicly available on\nhttp://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/.","url_abs":"http://arxiv.org/abs/1711.09081v2","url_pdf":"http://arxiv.org/pdf/1711.09081v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-extreme-cut-from-extreme-points-to","repo_url":"https://github.com/scaelles/DEXTR-KerasTensorflow","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deep-extreme-cut-from-extreme-points-to","repo_url":"https://github.com/scaelles/DEXTR-PyTorch","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"interactive-segmentation","task_name":"Interactive Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dextr","method_name":"DEXTR"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"ohem","method_name":"OHEM"},{"method_slug":"pyramid-pooling-module","method_name":"Pyramid Pooling Module"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"sgd-with-momentum","method_name":"SGD with Momentum"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[{"slug":"dextr","name":"DEXTR","full_name":"Deep Extreme Cut"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.09081","atlas_url":"https://app.syntology.ai/?focus=1711.09081","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}