{"ID":2890133,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19870","arxiv_id":"2507.19870","title":"OW-CLIP: Data-Efficient Visual Supervision for Open-World Object Detection via Human-AI Collaboration","abstract":"Open-world object detection (OWOD) extends traditional object detection to identifying both known and unknown object, necessitating continuous model adaptation as new annotations emerge. Current approaches face significant limitations: 1) data-hungry training due to reliance on a large number of crowdsourced annotations, 2) susceptibility to \"partial feature overfitting,\" and 3) limited flexibility due to required model architecture modifications. To tackle these issues, we present OW-CLIP, a visual analytics system that provides curated data and enables data-efficient OWOD model incremental training. OW-CLIP implements plug-and-play multimodal prompt tuning tailored for OWOD settings and introduces a novel \"Crop-Smoothing\" technique to mitigate partial feature overfitting. To meet the data requirements for the training methodology, we propose dual-modal data refinement methods that leverage large language models and cross-modal similarity for data generation and filtering. Simultaneously, we develope a visualization interface that enables users to explore and deliver high-quality annotations: including class-specific visual feature phrases and fine-grained differentiated images. Quantitative evaluation demonstrates that OW-CLIP achieves competitive performance at 89% of state-of-the-art performance while requiring only 3.8% self-generated data, while outperforming SOTA approach when trained with equivalent data volumes. A case study shows the effectiveness of the developed method and the improved annotation quality of our visualization system.","short_abstract":"Open-world object detection (OWOD) extends traditional object detection to identifying both known and unknown object, necessitating continuous model adaptation as new annotations emerge. Current approaches face significant limitations: 1) data-hungry training due to reliance on a large number of crowdsourced annotation...","url_abs":"https://arxiv.org/abs/2507.19870","url_pdf":"https://arxiv.org/pdf/2507.19870v1","authors":"[\"Junwen Duan\",\"Wei Xue\",\"Ziyao Kang\",\"Shixia Liu\",\"Jiazhi Xia\"]","published":"2025-07-26T08:58:56Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.HC\"]","methods":"[\"Language Model\"]","has_code":false}