{"refrec":{"BRefID":449924,"RR":"<b>Wong, Y.-K.; Liang, H.; Ma, Z.; Cheng, Y.; Zheng, Z.; Gotama, R.; Sebastian, P.; Sparks, L.D.; Yeung, S.K.</b> (2026). ORCA: Object Recognition and Comprehension for Archiving Marine Species, <b><i>in</i></b>: <i>2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 6-10 March 2026, Tucson, Arizona, USA.</i> pp. 1597-1609. <a href=\"https://dx.doi.org/10.1109/wacv61042.2026.00161\" target=\"_blank\">https://dx.doi.org/10.1109/wacv61042.2026.00161</a>","BEntID":447776,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":", <b><i>in</i></b>: <i>2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 6-10 March 2026, Tucson, Arizona, USA.</i> pp. 1597-1609. <a href=\"https://dx.doi.org/10.1109/wacv61042.2026.00161\" target=\"_blank\">https://dx.doi.org/10.1109/wacv61042.2026.00161</a>","DocTypID":17,"DocType":"Book chapters","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Wong, Y.-K.; Liang, H.; Ma, Z.; Cheng, Y.; Zheng, Z.; Gotama, R.; Sebastian, P.; Sparks, L.D.; Yeung, S.K.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Wong, Y.-K. <i>et al.</i>","Englishabstract":"Marine visual understanding is essential for monitoring and protecting marine ecosystems, enabling automatic and scalable biological surveys. However, progress is hindered by limited training data and the lack of a systematic task formulation that aligns domain-specific marine challenges with well-defined computer vision tasks, thereby limiting effective model application. To address this gap, we present ORCA, a multi-modal benchmark for marine research comprising 14,647 images from 478 species, with 42,217 bounding box annotations and 22,321 expert-verified instance captions. The dataset provides fine-grained visual and textual annotations that capture morphology-oriented attributes across diverse marine species. To catalyze methodological advances, we evaluate 18 state-of-the-art models on three tasks: object detection (closed-set and open-vocabulary), instance captioning, and visual grounding. Results highlight key challenges, including species diversity, morphological overlap, and specialized domain demands, underscoring the difficulty of marine understanding. ORCA thus establishes a comprehensive benchmark to advance research in marine domain.","AbstractOtherLang":null,"BibLvlCode":"AM","StandardTitle":"ORCA: Object Recognition and Comprehension for Archiving Marine Species","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-10-13 01:50:31.123495","timezone_type":1,"timezone":"+02:00"},"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2026,"MonPub":null,"DateUpdate":"2026-10-08","DateCreate":"2026-10-08","SecASFANote":null,"ConfID":null,"PeerRev":0,"VlizCoreFlag":1,"WoScode":null,"VABBcode":null,"OpenAcc":0,"DOI":"10.1109/wacv61042.2026.00161"},"refs":null,"anarec":{"AnaID":449924,"PubliDate":2026,"Pagination":"1597-1609","XtraPublOfAnaID":null,"ISBN":"979-8-3315-5511-5","Volume":null,"Issue":null,"BRefMon":449923,"BRefMonRR":"(2026). 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 6-10 March 2026, Tucson, Arizona, USA. 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