VL-SAM-v3: Memory-Guided Visual Priors for Open-World Object Detection
Open-world object detection aims to localize and recognize objects beyond a fixed closed-set label space. It is commonly divided into two categories, i.e., open-vocabulary detection, which assumes a predefined category list at test time, and open-ended detection, which requires generating candidate categories during the inference. Existing methods rely primarily on coarse textual semantics and parametric knowledge, which often provide insufficient visual evidence for fine-grained appearance variation, rare categories, and cluttered scenes. In this paper, we propose VL-SAM-v3, a unified framework that augments open-world detection with retrieval-grounded external visual memory. Specificall...
| Authors | Chih-Chung Liu, Zhiwei Lin, Yongtao Wang |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 9 May 2026 |
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