Abstract
Few-shot learning (FSL) has recently been extensively utilized to overcome the scarcity of training data in domain-specific visual recognition. In real-world scenarios, environmental factors such as complex backgrounds, varying lighting conditions, long-distance shooting, and moving targets often cause test images to exhibit numerous incomplete targets or noise disruptions. However, current research on evaluation datasets and methodologies has largely ignored the concept of "environmental robustness", which refers to maintaining consistent performance in complex and diverse physical environments. This neglect has led to a notable decline in the performance of FSL models during practical testing compared to their training performance. To bridge this gap, we introduce a new real-world multi-domain few-shot learning (RD-FSL) benchmark, which includes four domains and six evaluation datasets. The test images in this benchmark feature various challenging elements, such as camouflaged objects, small targets, and blurriness. Our evaluation experiments reveal that existing methods struggle to utilize training images effectively to generate accurate feature representations for challenging test images. To address this problem, we propose a novel conditional representation learning network (CRLNet) that integrates the interactions between training and testing images as conditional information in their respective representation processes. The main goal is to reduce intra-class variance or enhance inter-class variance at the feature representation level. Finally, comparative experiments reveal that CRLNet surpasses the current state-of-the-art methods, achieving performance improvements ranging from 6.83% to 16.98% across diverse settings and backbones. The source code and dataset are available at this https URL.
Abstract (translated)
最近，为了解决特定领域视觉识别中训练数据不足的问题，少量样本学习（Few-shot Learning, FSL）得到了广泛应用。在实际场景中，环境因素如复杂的背景、变化的光照条件、远距离拍摄以及移动目标等会导致测试图像出现大量的不完整目标或噪声干扰。然而，目前关于评估数据集和方法的研究大多忽视了“环境鲁棒性”的概念，即模型在复杂多变的真实环境中保持一致性能的能力。这种忽视导致FSL模型在实际应用中的表现与训练阶段相比显著下降。 为弥补这一差距，我们引入了一个新的基于真实世界的跨域少量样本学习（Real-world Multi-domain Few-shot Learning, RD-FSL）基准测试，其中包括四个领域和六个评估数据集。该测试基准中的图像包含了各种具有挑战性的元素，如伪装物体、小目标以及模糊图像。通过评估实验我们发现，现有方法在利用训练图像生成挑战性测试图像的准确特征表示方面存在困难。 为解决这一问题，我们提出了一种新的条件表征学习网络（Conditional Representation Learning Network, CRLNet），该网络将训练与测试图像之间的交互作为各自的表征过程中的条件信息。其主要目标是通过降低类内方差或增强类间方差来改进特征表示层面的表现。 最后，对比实验表明CRLNet超越了当前最先进的方法，在不同设置和骨干架构下实现了6.83%到16.98%的性能提升。源代码及数据集可在该链接获取。
URL
https://arxiv.org/abs/2502.01183
