针对复杂环境下的少样本分割，提出自适应注意力蒸馏方法提升鲁棒性。
Adaptive Attention Distillation for Robust Few-Shot Segmentation under Environmental Perturbations
- 通过对比支持图与查询图的共享语义，动态生成类别特定注意力。
- 在八大数据集上平均提升mIoU 3.3%~8.5%，显著增强泛化能力。
- 适合医疗图像、工业质检等真实场景中少样本目标分割任务。
少样本分割（FSS）旨在从少量样本中快速学习新类别，实现对未见图像中特定目标的分割，已广泛应用于医疗诊断和工业检测等领域。然而，现有研究普遍忽视真实场景中的复杂环境因素，如光照变化、背景干扰和摄像机视角差异，这些因素会显著增加测试图像的难度，导致实验室训练模型难以满足实际部署需求。为此，本文提出一种环境鲁棒的少样本分割设定，显式引入运动模糊、小目标和伪装目标等挑战性测试案例，以提升模型在真实动态环境中的鲁棒性。构建了首个环境鲁棒的少样本分割基准（ER-FSS），涵盖八个跨真实场景的数据集。同时提出自适应注意力蒸馏（AAD）方法，通过反复对比和提炼支持图与查询图之间的关键共享语义，生成类别特定注意力，强化模型在复杂环境下聚焦正确目标的能力。大量实验表明，该方法在所有数据集和设置下均提升mIoU 3.3%–8.5%，展现出优异性能与强泛化能力。代码与数据集已开源：https://github.com/guoqianyu-alberta/Adaptive-Attention-Distillation-for-FSS。
原文摘要 · Abstract (English)
Few-shot segmentation (FSS) aims to rapidly learn novel class concepts from limited examples to segment specific targets in unseen images, and has been widely applied in areas such as medical diagnosis and industrial inspection. However, existing studies largely overlook the complex environmental factors encountered in real world scenarios-such as illumination, background, and camera viewpoint-which can substantially increase the difficulty of test images. As a result, models trained under laboratory conditions often fall short of practical deployment requirements. To bridge this gap, in this paper, an environment-robust FSS setting is introduced that explicitly incorporates challenging test cases arising from complex environments-such as motion blur, small objects, and camouflaged targets-to enhance model's robustness under realistic, dynamic conditions. An environment robust FSS benchmark (ER-FSS) is established, covering eight datasets across multiple real world scenarios. In addition, an Adaptive Attention Distillation (AAD) method is proposed, which repeatedly contrasts and distills key shared semantics between known (support) and unknown (query) images to derive class-specific attention for novel categories. This strengthens the model's ability to focus on the correct targets in complex environments, thereby improving environmental robustness. Comparative experiments show that AAD improves mIoU by 3.3% - 8.5% across all datasets and settings, demonstrating superior performance and strong generalization. The source code and dataset are available at: https://github.com/guoqianyu-alberta/Adaptive-Attention-Distillation-for-FSS.
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