{"ID":2825489,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21150","arxiv_id":"2512.21150","title":"ORCA: Object Recognition and Comprehension for Archiving Marine Species","abstract":"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. Project Page: http://orca.hkustvgd.com/.","short_abstract":"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 visi...","url_abs":"https://arxiv.org/abs/2512.21150","url_pdf":"https://arxiv.org/pdf/2512.21150v1","authors":"[\"Yuk-Kwan Wong\",\"Haixin Liang\",\"Zeyu Ma\",\"Yiwei Chen\",\"Ziqiang Zheng\",\"Rinaldi Gotama\",\"Pascal Sebastian\",\"Lauren D. Sparks\",\"Sai-Kit Yeung\"]","published":"2025-12-24T12:36:57Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","project_urls":"[\"http://orca.hkustvgd.com/\"]","has_code":false}