VK-Det: Visual Knowledge Guided Prototype Learning for Open-Vocabulary Aerial Object Detection

• URL: http://arxiv.org/abs/2511.18075v1
• Date: Sat, 22 Nov 2025 14:19:59 GMT
• Title: VK-Det: Visual Knowledge Guided Prototype Learning for Open-Vocabulary Aerial Object Detection
• Authors: Jianhang Yao, Yongbin Zheng, Siqi Lu, Wanying Xu, Peng Sun,
• Abstract summary: We propose a text-guided open-vocabulary object $textbfDet$ection framework.<n>We discover and leverage vision encoder's inherent informative region perception to attain fine-grained localization and adaptive distillation.<n>Experiments show state-of-the-art performance, achieving 30.1 $mathrmmAPN$ on DIOR and 23.3 $mathrmmAPN$ on DOTA, outperforming even extra supervised methods.
• Score: 6.72903082348742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To identify objects beyond predefined categories, open-vocabulary aerial object detection (OVAD) leverages the zero-shot capabilities of visual-language models (VLMs) to generalize from base to novel categories. Existing approaches typically utilize self-learning mechanisms with weak text supervision to generate region-level pseudo-labels to align detectors with VLMs semantic spaces. However, text dependence induces semantic bias, restricting open-vocabulary expansion to text-specified concepts. We propose $\textbf{VK-Det}$, a $\textbf{V}$isual $\textbf{K}$nowledge-guided open-vocabulary object $\textbf{Det}$ection framework $\textit{without}$ extra supervision. First, we discover and leverage vision encoder's inherent informative region perception to attain fine-grained localization and adaptive distillation. Second, we introduce a novel prototype-aware pseudo-labeling strategy. It models inter-class decision boundaries through feature clustering and maps detection regions to latent categories via prototype matching. This enhances attention to novel objects while compensating for missing supervision. Extensive experiments show state-of-the-art performance, achieving 30.1 $\mathrm{mAP}^{N}$ on DIOR and 23.3 $\mathrm{mAP}^{N}$ on DOTA, outperforming even extra supervised methods.

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