The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning. However, with the availability of massive labeled data, a natural question emerges: how to train a better model with both self and full supervision signals? In this paper, we propose Omni-suPErvised Representation leArning with hierarchical supervisions (OPERA) as a solution. We provide a unified perspective of supervisions from labeled and unlabeled data and propose a unified framework of fully supervised and self-supervised learning. We extract a set of hierarchical proxy representations for each image and impose self and full supervisions on the corresponding proxy representations. Extensive experiments on both convolutional neural networks and vision transformers demonstrate the superiority of OPERA in image classification, segmentation, and object detection. Code is available at: https://github.com/wangck20/OPERA.

该研究提出了一种名为OPERA的Omni-suPErvised Representation leArning方法，通过全面利用受监督和无监督的数据，将自监督学习和完全监督学习统一到一个框架中，以获得更好的模型性能。该方法通过使用层次化监督对每个图像提取一组代理表示，并在相应的代理表示上施加自监督和完全监督信号，展现了在图像分类、分割和目标检测上性能的稳定提升。

OPERA: 带有层级监督的全方位监督表示学习