{"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/1st-place-solution-of-lvis-challenge-2020-a","title":"1st Place Solution of LVIS Challenge 2020: A Good Box is not a Guarantee of a Good Mask","arxiv_id":"2009.01559","date":"2020-09-03","proceeding":null,"authors":["Jingru Tan","Gang Zhang","Hanming Deng","Changbao Wang","Lewei Lu","Quanquan Li","Jifeng Dai"],"abstract":"This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020. In this work, two characteristics of LVIS dataset are mainly considered: the long-tailed distribution and high quality instance segmentation mask. We adopt a two-stage training pipeline. In the first stage, we incorporate EQL and self-training to learn generalized representation. In the second stage, we utilize Balanced GroupSoftmax to promote the classifier, and propose a novel proposal assignment strategy and a new balanced mask loss for mask head to get more precise mask predictions. Finally, we achieve 41.5 and 41.2 AP on LVIS v1.0 val and test-dev splits respectively, outperforming the baseline based on X101-FPN-MaskRCNN by a large margin.","url_abs":"https://arxiv.org/abs/2009.01559v1","url_pdf":"https://arxiv.org/pdf/2009.01559v1.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":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/instance-segmentation-on-lvis-v1-0-test-dev","task":"Instance Segmentation","dataset":"LVIS v1.0 test-dev","model":"R50-FPN-MaskRCNN-TTA","rank_in_archive_order":1,"of":1,"metrics":{"mask AP":"41.23"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2009.01559","atlas_url":"https://app.syntology.ai/?focus=2009.01559","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}