发表机构
The University of Warwick; Wuhan University（华威大学; 武汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TriNoL框架通过将未标记样本按伪标签置信度分配给三个LoRA专家，实现半监督视觉基础模型适配，提升了对带噪监督的鲁棒性且训练成本较低。
AI 中文摘要
半监督视觉基础模型（Vision Foundation Model, VFM）适配通常会冻结预训练骨干网络，仅更新LoRA等轻量模块。然而伪标签的可靠性参差不齐，单一LoRA适配器需在同一低秩空间中吸收可靠、模糊及带噪梯度，这会导致VFM适配对伪标签噪声敏感。我们提出TriNoL，即面向半监督VFM适配的带噪标签三重专家学习框架。TriNoL将未标记样本划分为三个置信度区域，并分配给三个LoRA专家：针对高置信度伪标签的正专家、针对中等置信度模糊样本的对齐专家，以及针对低置信度带噪样本的负专家。VFM骨干网络保持冻结状态，仅更新LoRA专家和分类器头。通过将不同伪标签可靠性区域分离为专门的适配路径，TriNoL在保持训练成本较低的同时，提升了对带噪监督的鲁棒性。
英文摘要
Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. However, pseudo-labels have mixed reliability, and a single LoRA adapter must absorb reliable, ambiguous, and noisy gradients in the same low-rank space. This can make VFM adaptation sensitive to pseudo-label noise. We propose \textbf{TriNoL}, a \textbf{Tri}ple-expert learning framework from \textbf{No}isy \textbf{L}abels for semi-supervised VFM adaptation. TriNoL routes unlabeled samples into three confidence regions and assigns them to three LoRA experts: a Positive Expert for high-confidence pseudo-labels, an Alignment Expert for medium-confidence ambiguous samples, and a Negative Expert for low-confidence noisy samples. The VFM backbone remains frozen, and only the LoRA experts and classifier head are updated. By separating different pseudo-label reliability regions into specialized adaptation paths, TriNoL improves robustness to noisy supervision while keeping the training cost low.
CommentsAccepted for publication at the British Machine Vision Conference (BMVC) 2026. Official list of accepted papers: https://bmvc2026.bmva.org/programme/accepted_papers/
