Abstract
Self-supervised learning (SSL) has demonstrated its effectiveness in learning representations through comparison methods that align with human intuition. However, mainstream SSL methods heavily rely on high body datasets with single label, such as ImageNet, resulting in intolerable pre-training overhead. Besides, more general multi-label images are frequently overlooked in SSL, despite their potential for richer semantic information and broader applicability in downstream scenarios. Therefore, we tailor the mainstream SSL approach to guarantee excellent representation learning capabilities using fewer multi-label images. Firstly, we propose a block-wise augmentation module aimed at extracting additional potential positive view pairs from multi-label images. Subsequently, an image-aware contrastive loss is devised to establish connections between these views, thereby facilitating the extraction of semantically consistent representations. Comprehensive linear fine-tuning and transfer learning validate the competitiveness of our approach despite challenging sample quality and quantity.
Abstract (translated)
自监督学习（SSL）通过与人类直觉相符合的比较方法展示了其在表示学习方面的有效性。然而，主流的SSL方法严重依赖于具有单一标签的大规模数据集，如ImageNet，这导致了无法接受的预训练开销。此外，尽管多标签图像具有更丰富的语义信息和下游应用场景中的广泛适用性，但在SSL中却常常被忽视。因此，我们将主流的SSL方法进行调整，以利用较少的多标签图像确保卓越的表示学习能力。 首先，我们提出了一种基于块增强模块的方法，旨在从多标签图像中提取额外潜在的正向视图对。随后，设计了一种感知图像对比损失来建立这些视图之间的联系，从而促进语义一致性的表示抽取。通过全面的线性微调和迁移学习验证了我们的方法在具有挑战性的样本质量和数量条件下仍具备竞争力。
URL
https://arxiv.org/abs/2506.23156
