Few-shot segmentation aims to train a segmentation model that can fast adapt to a novel task for which only a few annotated images are provided. Most recent models have adopted a prototype-based paradigm for few-shot inference. These approaches may have limited generalization capacity beyond the standard 1- or 5-shot settings. In this paper, we closely examine and reevaluate the fine-tuning based learning scheme that fine-tunes the classification layer of a deep segmentation network pre-trained on diverse base classes. To improve the generalizability of the classification layer optimized with sparsely annotated samples, we introduce an instance-aware data augmentation (IDA) strategy that augments the support images based on the relative sizes of the target objects. The proposed IDA effectively increases the support set's diversity and promotes the distribution consistency between support and query images. On the other hand, the large visual difference between query and support images may hinder knowledge transfer and cripple the segmentation performance. To cope with this challenge, we introduce the local consensus guided cross attention (LCCA) to align the query feature with support features based on their dense correlation, further improving the model's generalizability to the query image. The significant performance improvements on the standard few-shot segmentation benchmarks PASCAL-$5^i$ and COCO-$20^i$ verify the efficacy of our proposed method.

翻译：小样本分割旨在训练一个分割模型，使其能够快速适应仅提供少量标注图像的新任务。近期大多数模型采用基于原型的方法进行小样本推理，但这些方法在标准1样本或5样本设置之外可能存在泛化能力受限的问题。本文深入审视并重新评估了基于微调的学习方案，即对预训练于多样基类上的深度分割网络分类层进行微调。为提升基于稀疏标注样本优化的分类层的泛化能力，我们引入了一种实例感知数据增强（IDA）策略，该策略根据目标物体的相对大小对支持图像进行增强。所提出的IDA能有效增加支持集的多样性，并促进支持图像与查询图像之间的分布一致性。另一方面，查询图像与支持图像之间巨大的视觉差异可能阻碍知识迁移并削弱分割性能。为应对这一挑战，我们提出了局部共识引导的交叉注意力（LCCA）机制，基于密集相关性将查询特征与支持特征对齐，从而进一步改善模型对查询图像的泛化能力。在标准小样本分割基准PASCAL-\(5^i\)和COCO-\(20^i\)上的显著性能提升验证了所提方法的有效性。