{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:38:38Z","timestamp":1773801518597,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Floor plan recognition requires accurate segmentation and classification of entrance doors, outer contours (walls and windows) and inner contours (various room types) , despite strong spatial dependencies and large stylistic differences between different datasets. To overcome these challenges, we propose FloorPlanFormer, a multi-task learning network divided into three phases: the first phase introduces a Swin Transformer backbone with a pixel decoder to extract fine-grained pixel-level semantics; the second phase employs prompt encoder and mask decoder, and a novel Global Contextual Attention Module (GCAM) is designed to generate clear, high-quality outer contour masks; the third stage uses mask transformer decoder to recognize targets and designs a Masked Feature Refinement Module (MFRM) to accurately delineate the inner contour by modeling the relationship between the local inner and outer contours. Finally, we constructed FloorPlan8K, a dataset containing 8200 images and 77434 instances, on which our model was trained and evaluated, and the results greatly outperformed the state-of-the-art general segmentation methods and specialized methods.<\/jats:p>","DOI":"10.1609\/aaai.v40i9.37625","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:38:51Z","timestamp":1773790731000},"page":"6916-6924","source":"Crossref","is-referenced-by-count":0,"title":["FloorPlanFormer: Multi-Task Transformer Network for Floor Plan Recognition with Outer-to-Inner Feature Refinement"],"prefix":"10.1609","volume":"40","author":[{"given":"Yun","family":"Liang","sequence":"first","affiliation":[]},{"given":"ZiHao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Run","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Yishen","family":"Lin","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37625\/41587","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37625\/41587","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:38:51Z","timestamp":1773790731000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i9.37625","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}