{"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/attention-guided-unified-network-for-panoptic","title":"Attention-guided Unified Network for Panoptic Segmentation","arxiv_id":"1812.03904","date":"2018-12-10","proceeding":"CVPR 2019 6","authors":["Yanwei Li","Xinze Chen","Zheng Zhu","Lingxi Xie","Guan Huang","Dalong Du","Xingang Wang"],"abstract":"This paper studies panoptic segmentation, a recently proposed task which\nsegments foreground (FG) objects at the instance level as well as background\n(BG) contents at the semantic level. Existing methods mostly dealt with these\ntwo problems separately, but in this paper, we reveal the underlying\nrelationship between them, in particular, FG objects provide complementary cues\nto assist BG understanding. Our approach, named the Attention-guided Unified\nNetwork (AUNet), is a unified framework with two branches for FG and BG\nsegmentation simultaneously. Two sources of attentions are added to the BG\nbranch, namely, RPN and FG segmentation mask to provide object-level and\npixel-level attentions, respectively. Our approach is generalized to different\nbackbones with consistent accuracy gain in both FG and BG segmentation, and\nalso sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes\n(59.0% PQ) benchmarks.","url_abs":"http://arxiv.org/abs/1812.03904v2","url_pdf":"http://arxiv.org/pdf/1812.03904v2.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":[],"tasks":[{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"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":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"resnext","method_name":"ResNeXt"},{"method_slug":"resnext-block","method_name":"ResNeXt Block"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/panoptic-segmentation-on-coco-test-dev","task":"Panoptic Segmentation","dataset":"COCO test-dev","model":"AUNet (ResNext-152-FPN)","rank_in_archive_order":25,"of":38,"metrics":{"PQ":"46.5","PQst":"32.5","PQth":"55.8"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-coco-test-dev","task":"Panoptic Segmentation","dataset":"COCO test-dev","model":"AUNet (ResNet-152-FPN)","rank_in_archive_order":26,"of":38,"metrics":{"PQ":"45.5","PQst":"31.6","PQth":"54.7"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-coco-test-dev","task":"Panoptic Segmentation","dataset":"COCO test-dev","model":"AUNet (ResNet-101-FPN)","rank_in_archive_order":27,"of":38,"metrics":{"PQ":"45.2","PQst":"31.3","PQth":"54.4"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-cityscapes-val","task":"Panoptic Segmentation","dataset":"Cityscapes val","model":"AUNet (ResNet-101-FPN)","rank_in_archive_order":30,"of":37,"metrics":{"AP":"34.4","PQ":"59.0","PQst":"62.1","PQth":"54.8","mIoU":"75.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.03904","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}