Reference-based Category Discovery: Unsupervised Object Detection with Category Awareness
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866909017092128768 |
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| author | Li, Yichen Liu, Qiankun Fu, Ying |
| author_facet | Li, Yichen Liu, Qiankun Fu, Ying |
| contents | Traditional one-shot detection methods have addressed the closed-set problem in object detection, but the high cost of data annotation remains a critical challenge. General unsupervised methods generate pseudo boxes without category labels, thus failing to achieve category-aware classification. To overcome these limitations, we propose Reference-based Category Discovery (RefCD), an unsupervised detector that enables category-aware\footnotemark[1] detection without any manually annotated labels. It leverages feature similarity between predicted objects and unlabeled reference images. Unlike previous unsupervised methods that lack category guidance and one-shot methods which require labeled data, RefCD introduces a carefully designed feature similarity loss to explicitly guide the learning of potential category-specific features. Additionally, RefCD supports category-agnostic detection without reference images, serving as a unified framework. Comprehensive quantitative and qualitative analysis of category-aware and category-agnostic detection results demonstrates its effectiveness, and RefCD can learn category information in an unsupervised paradigm even without category labels. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_04606 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reference-based Category Discovery: Unsupervised Object Detection with Category Awareness Li, Yichen Liu, Qiankun Fu, Ying Computer Vision and Pattern Recognition Artificial Intelligence 68U I.4.8 Traditional one-shot detection methods have addressed the closed-set problem in object detection, but the high cost of data annotation remains a critical challenge. General unsupervised methods generate pseudo boxes without category labels, thus failing to achieve category-aware classification. To overcome these limitations, we propose Reference-based Category Discovery (RefCD), an unsupervised detector that enables category-aware\footnotemark[1] detection without any manually annotated labels. It leverages feature similarity between predicted objects and unlabeled reference images. Unlike previous unsupervised methods that lack category guidance and one-shot methods which require labeled data, RefCD introduces a carefully designed feature similarity loss to explicitly guide the learning of potential category-specific features. Additionally, RefCD supports category-agnostic detection without reference images, serving as a unified framework. Comprehensive quantitative and qualitative analysis of category-aware and category-agnostic detection results demonstrates its effectiveness, and RefCD can learn category information in an unsupervised paradigm even without category labels. |
| title | Reference-based Category Discovery: Unsupervised Object Detection with Category Awareness |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68U I.4.8 |
| url | https://arxiv.org/abs/2605.04606 |