{"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/scaling-wide-residual-networks-for-panoptic","title":"Scaling Wide Residual Networks for Panoptic Segmentation","arxiv_id":"2011.11675","date":"2020-11-23","proceeding":null,"authors":["Liang-Chieh Chen","Huiyu Wang","Siyuan Qiao"],"abstract":"The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant of the Residual Networks (ResNets) by stacking a small number of residual blocks with large channel sizes, have demonstrated outstanding performance on multiple dense prediction tasks. However, since proposed, the Wide-ResNet architecture has barely evolved over the years. In this work, we revisit its architecture design for the recent challenging panoptic segmentation task, which aims to unify semantic segmentation and instance segmentation. A baseline model is obtained by incorporating the simple and effective Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets. Its network capacity is further scaled up or down by adjusting the width (i.e., channel size) and depth (i.e., number of layers), resulting in a family of SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such a simple scaling scheme, coupled with grid search, identifies several SWideRNets that significantly advance state-of-the-art performance on panoptic segmentation datasets in both the fast model regime and strong model regime.","url_abs":"https://arxiv.org/abs/2011.11675v2","url_pdf":"https://arxiv.org/pdf/2011.11675v2.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":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/panoptic-segmentation-on-coco-test-dev","task":"Panoptic Segmentation","dataset":"COCO test-dev","model":"Panoptic-DeepLab (SWideRNet-[1, 1, 4], multi-scale)","rank_in_archive_order":24,"of":38,"metrics":{"PQ":"46.5","PQst":"38.2","PQth":"52.0"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-cityscapes-test","task":"Panoptic Segmentation","dataset":"Cityscapes test","model":"Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary, multi-scale)","rank_in_archive_order":2,"of":10,"metrics":{"PQ":"67.8"},"uses_additional_data":true},{"leaderboard":"/sota/panoptic-segmentation-on-cityscapes-val","task":"Panoptic Segmentation","dataset":"Cityscapes val","model":"Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary Vistas, multi-scale)","rank_in_archive_order":3,"of":37,"metrics":{"AP":"46.8","PQ":"69.6","mIoU":"85.3"},"uses_additional_data":true},{"leaderboard":"/sota/panoptic-segmentation-on-cityscapes-val","task":"Panoptic Segmentation","dataset":"Cityscapes val","model":"Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary Vistas, single-scale)","rank_in_archive_order":6,"of":37,"metrics":{"AP":"42.8","PQ":"68.5","mIoU":"84.6"},"uses_additional_data":true},{"leaderboard":"/sota/panoptic-segmentation-on-mapillary-val","task":"Panoptic Segmentation","dataset":"Mapillary val","model":"Panoptic-DeepLab (SWideRNet-(1, 1, 4.5), multi-scale)","rank_in_archive_order":4,"of":13,"metrics":{"PQ":"44.8","PQst":"51.9","PQth":"39.3","mIoU":"60.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2011.11675","atlas_url":"https://app.syntology.ai/?focus=2011.11675","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}