论文标题
在混合图像上有监督的对比度学习，以进行长尾识别
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition
论文作者
论文摘要
现实世界中的数据通常具有长尾巴的分布，在培训类别中，每个类别的样本数量不相等。不平衡的数据形成了一个有偏见的特征空间，这会恶化识别模型的性能。在本文中，我们提出了一种新型的长尾识别方法，以平衡潜在特征空间。首先，我们引入了基于混合的数据增强技术，以减少长尾数据的偏差。此外，我们提出了一种新的监督对比学习方法，称为混合班级（SMC）的对比度学习，用于混合图像。 SMC根据原始图像的类标签创建了一组阳性。阳性的组合比率加权了训练损失的积极因素。基于类混合的损失的SMC探索了更多样化的数据空间，从而增强了模型的概括能力。对各种基准测试的广泛实验表明了我们一阶段训练方法的有效性。
Real-world data often have a long-tailed distribution, where the number of samples per class is not equal over training classes. The imbalanced data form a biased feature space, which deteriorates the performance of the recognition model. In this paper, we propose a novel long-tailed recognition method to balance the latent feature space. First, we introduce a MixUp-based data augmentation technique to reduce the bias of the long-tailed data. Furthermore, we propose a new supervised contrastive learning method, named Supervised contrastive learning on Mixed Classes (SMC), for blended images. SMC creates a set of positives based on the class labels of the original images. The combination ratio of positives weights the positives in the training loss. SMC with the class-mixture-based loss explores more diverse data space, enhancing the generalization capability of the model. Extensive experiments on various benchmarks show the effectiveness of our one-stage training method.
