发表机构
University of Warwick; Universitat Autonoma de Barcelona（华威大学; 巴塞罗那自治大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReCalMatch提出利用多语义原型校准伪标签可靠性，结合视觉-语义一致性、置信度和熵加权，提升半监督细粒度识别，在低标签率下显著优于现有SSL方法。
AI 中文摘要
半监督细粒度视觉识别极易受到过度自信的伪标签错误的影响：视觉上相似的类别经常产生高置信度但错误的预测，而一致性正则化在整个训练过程中会强化这些错误。现有的半监督学习（SSL）方法几乎完全从视觉分类器本身估计伪标签的可靠性——最大概率、自适应阈值或熵——这些信号无法判断预测类别是否与视觉表示在语义上兼容。我们提出了ReCalMatch，一个用于半监督细粒度识别的可靠性校准语义框架。ReCalMatch不将文本语义视为辅助监督，而是将多方面语义原型作为伪标签学习的校准证据。我们从类名和特定领域的语义方面构建类条件语义原型，并测量每个未标记嵌入与其伪标签原型之间的视觉-语义一致性得分。该一致性得分与预测置信度和熵相结合，形成一个单一的可靠性权重，用于降低视觉上自信但语义不一致的伪标签的权重。语义一致性项和语义边际正则化器进一步在有限标签下增强原型的可分离性。在CUB-200-2011、Stanford Dogs、NABirds和iNaturalist18上的大量实验表明，ReCalMatch持续改进强SSL基线，在伪标签噪声最严重的低标签率场景中提升最大。
英文摘要
Semi-supervised fine-grained visual recognition is highly vulnerable to overconfident pseudo-label errors: visually similar categories frequently produce high-confidence yet incorrect predictions, and consistency regularization then reinforces these errors throughout training. Existing semi-supervised learning (SSL) methods estimate pseudo-label reliability almost entirely from the visual classifier itself---maximum probability, adaptive thresholds, or entropy---signals that remain blind to whether a predicted class is \emph{semantically} compatible with the visual representation. We propose \textbf{ReCalMatch}, a reliability-calibrated semantic framework for semi-supervised fine-grained recognition. Rather than treating textual semantics as auxiliary supervision, ReCalMatch uses multi-aspect semantic prototypes as \emph{calibration evidence} for pseudo-label learning. We construct class-conditioned semantic prototypes from class names and domain-specific semantic aspects, and measure a \emph{visual--semantic agreement} score between each unlabeled embedding and its pseudo-label prototype. This agreement is combined with prediction confidence and entropy into a single reliability weight that down-weights pseudo-labels that are visually confident but semantically inconsistent. A semantic consistency term and a semantic margin regularizer further sharpen prototype separability under limited labels. Extensive experiments on CUB-200-2011, Stanford Dogs, NABirds, and iNaturalist18 show that ReCalMatch consistently improves strong SSL baselines, with the largest gains in low-label regimes where pseudo-label noise is most severe.
CommentsAccepted for publication at the British Machine Vision Conference (BMVC) 2026. Official list of accepted papers:https://bmvc2026.bmva.org/programme/accepted_papers/
