{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:49:33Z","timestamp":1773802173424,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"15","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Open-set object detection (OSOD) aims to recognize known object categories while localizing previously unseen instances. However, real-world scenarios often involve co-occurring domain shifts and novel object categories. Existing OSOD methods typically overlook domain shifts, relying on source-trained representations that entangle domain-specific style with semantic content, thereby hindering generalization to both unseen domains and novel categories. To address this challenge, we propose a unified framework, termed DecOmpose and ATtribute (DOAT), which disentangles domain-specific style from semantic structure, thereby facilitating generalizable object detection. DOAT employs wavelet-based feature decomposition to separate style information from high-frequency structural details, thus enabling an explicit separation of domain and category shifts. To account for domain shift, the low-frequency components are perturbed within a style subspace to simulate diverse domain appearances. For unknown object discovery, the high-frequency components are utilized to estimate objectness scores via an attribution mechanism that fuses wavelet energy with semantic distance to known-category prototypes. Extensive experiments on standard open-set benchmarks have demonstrated the superior generalization performance of DOAT.<\/jats:p>","DOI":"10.1609\/aaai.v40i15.38220","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:18:58Z","timestamp":1773793138000},"page":"12286-12294","source":"Crossref","is-referenced-by-count":0,"title":["Decompose and Attribute: Boosting Generalizable Open-Set Object Detection via Objectness Score"],"prefix":"10.1609","volume":"40","author":[{"given":"Yuxuan","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Lichen","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Luyao","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Chaoqi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zheyuan","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xinghao","family":"Ding","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\/38220\/42182","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38220\/42182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:18:59Z","timestamp":1773793139000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38220"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i15.38220","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]]}}}