Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and they mostly rely on either a dedicated dense matching mechanism or a costly unsupervised object discovery module. This paper shows that instead of hinging on these strenuous operations, quality image representations can be learned by treating scene/multi-label image SSL simply as a multi-label classification problem, which greatly simplifies the learning framework. Specifically, multiple binary pseudo-labels are assigned for each input image by comparing its embeddings with those in two dictionaries, and the network is optimized using the binary cross entropy loss. The proposed method is named Multi-Label Self-supervised learning (MLS). Visualizations qualitatively show that clearly the pseudo-labels by MLS can automatically find semantically similar pseudo-positive pairs across different images to facilitate contrastive learning. MLS learns high quality representations on MS-COCO and achieves state-of-the-art results on classification, detection and segmentation benchmarks. At the same time, MLS is much simpler than existing methods, making it easier to deploy and for further exploration.

翻译：自监督学习（SSL）方法近年来在面向场景图像的应用中迅速发展，这些方法大多依赖专用的密集匹配机制或高成本的无监督目标检测模块。本文证明，无需依赖这些繁复操作，通过将场景/多标签图像自监督学习简化为多标签分类问题，即可学习到高质量的图像表征，从而极大简化学习框架。具体而言，通过将每张输入图像的嵌入与两个字典中的嵌入进行对比，为每个输入图像分配多个二值伪标签，并使用二元交叉熵损失优化网络。所提出方法命名为多标签自监督学习（MLS）。可视化结果清晰表明，MLS生成的伪标签能够自动在不同图像间发现语义相似的伪正样本对，以促进对比学习。MLS在MS-COCO数据集上学习到高质量表征，并在分类、检测和分割基准测试中达到当前最优结果。同时，MLS相比现有方法更为简洁，更易于部署和进一步探索。