Real-world fine-grained visual retrieval often requires discovering a rare concept from large unlabeled collections with minimal supervision. This is especially critical in biodiversity monitoring, ecological studies, and long-tailed visual domains, where the target may represent only a tiny fraction of the data, creating highly imbalanced binary problems. Interactive retrieval with relevance feedback offers a practical solution: starting from a small query, the system selects candidates for binary user annotation and iteratively refines a lightweight classifier. While Active Learning (AL) is commonly used to guide selection, conventional AL assumes symmetric class priors and large annotation budgets, limiting effectiveness in imbalanced, low-budget, low-latency settings. We introduce Positive-First Most Ambiguous (PF-MA), a simple yet effective AL criterion that explicitly addresses the class imbalance asymmetry: it prioritizes near-boundary samples while favoring likely positives, enabling rapid discovery of subtle visual categories while maintaining informativeness. Unlike standard methods that oversample negatives, PF-MA consistently returns small batches with a high proportion of relevant samples, improving early retrieval and user satisfaction. To capture retrieval diversity, we also propose a class coverage metric that measures how well selected positives span the visual variability of the target class. Experiments on long-tailed datasets, including fine-grained botanical data, demonstrate that PF-MA consistently outperforms strong baselines in both coverage and classifier performance, across varying class sizes and descriptors. Our results highlight that aligning AL with the asymmetric and user-centric objectives of interactive fine-grained retrieval enables simple yet powerful solutions for retrieving rare and visually subtle categories in realistic human-in-the-loop settings.

翻译：摘要：面向现实世界的细粒度视觉检索，常需从大规模无标注数据中以极少的监督信息发现稀有概念。这在生物多样性监测、生态学研究及长尾视觉领域尤为关键——目标类别可能仅占数据中的极小比例，从而形成高度不平衡的二分类问题。基于相关反馈的交互式检索提供了一种实用方案：从少量查询出发，系统选取候选样本供用户进行二值标注，并迭代优化轻量级分类器。尽管主动学习常被用于指导样本选择，但传统主动学习假设对称类别先验与大量标注预算，限制了其在不平衡、低预算、低延迟场景下的有效性。我们提出"正向优先最模糊"准则——一种简洁高效的主动学习指标，显式应对类别不平衡的非对称性：优先关注决策边界附近的样本，同时倾向于潜在正例，从而在保持信息量的同时快速发现细微视觉类别。不同于传统方法过度采样负例，PF-MA持续返回小批量且高比例相关样本的集合，显著提升早期检索效果与用户满意度。为衡量检索多样性，我们进一步提出类别覆盖度指标，量化所选正例对目标类别视觉变异空间的覆盖程度。在包含细粒度植物数据的长尾数据集上的实验表明，PF-MA在不同类别规模与特征描述子条件下，始终在覆盖度与分类器性能方面显著优于强基线方法。研究结果揭示：通过使主动学习与交互式细粒度检索的非对称性、用户中心目标对齐，可在现实人机协同设定下催生出简洁而有效的稀有细微类别检索方案。