Abstract
Cutmix-based data augmentation, which uses a cut-and-paste strategy, has shown remarkable generalization capabilities in deep learning. However, existing methods primarily consider global semantics with image-level constraints, which excessively reduces attention to the discriminative local context of the class and leads to a performance improvement bottleneck. Moreover, existing methods for generating augmented samples usually involve cutting and pasting rectangular or square regions, resulting in a loss of object part information. To mitigate the problem of inconsistency between the augmented image and the generated mixed label, existing methods usually require double forward propagation or rely on an external pre-trained network for object centering, which is inefficient. To overcome the above limitations, we propose LGCOAMix, an efficient context-aware and object-part-aware superpixel-based grid blending method for data augmentation. To the best of our knowledge, this is the first time that a label mixing strategy using a superpixel attention approach has been proposed for cutmix-based data augmentation. It is the first instance of learning local features from discriminative superpixel-wise regions and cross-image superpixel contrasts. Extensive experiments on various benchmark datasets show that LGCOAMix outperforms state-of-the-art cutmix-based data augmentation methods on classification tasks, {and weakly supervised object location on CUB200-2011.} We have demonstrated the effectiveness of LGCOAMix not only for CNN networks, but also for Transformer networks. Source codes are available at this https URL.
Abstract (translated)
基于Cutmix的数据增强方法，利用剪切和粘贴策略，在深度学习中表现出卓越的泛化能力。然而，现有的方法主要关注全局语义与图像级别的约束条件，这极大地减少了对类别鉴别性局部上下文的关注，并导致性能改进瓶颈。此外，现有生成增强样本的方法通常涉及切割和粘贴矩形或正方形区域，从而导致物体部分信息丢失。为解决增强图像与其生成的混合标签之间的一致性问题，现有的方法通常需要双向前传播或者依赖外部预训练网络进行目标定位，这在效率上显得不足。 为了克服上述限制，我们提出了LGCOAMix，这是一种高效且具备上下文感知和对象部件感知能力的超像素网格融合数据增强方法。据我们所知，这是首次提出使用超像素注意机制来改进基于Cutmix的数据增强策略的方法。它也是第一次尝试从有区分性的超像素级区域中学习局部特征并利用跨图像的超像素对比度。 在多个基准数据集上的广泛实验表明，LGCOAMix在分类任务上优于现有的最先进的基于Cutmix的数据增强方法，并且在CUB200-2011弱监督目标定位方面也表现出色。我们已经证明了LGCOAMix不仅适用于CNN网络，还适用于Transformer网络。源代码可以在[提供链接的位置]获取。 请注意，上述文本中的“{and weakly supervised object location on CUB200-2011.}”可能是一个格式或引用错误，我假设您希望这段信息包含在最终的翻译中，并因此将其直接译出。如果有其他特定需求，请告知调整。
URL
https://arxiv.org/abs/2512.00130
