Model pre-training has become essential in various recognition tasks.
Meanwhile, with the remarkable advancements in image generation models,
pre-training methods utilizing generated images have also emerged given their
ability to produce unlimited training data. However, while existing methods
utilizing generated images excel in classification, they fall short in more
practical tasks, such as human pose estimation. In this paper, we have
experimentally demonstrated it and propose the generation of visually distinct
images with identical human poses. We then propose a novel multi-positive
contrastive learning, which optimally utilize the previously generated images
to learn structural features of the human body. We term the entire learning
pipeline as GenPoCCL. Despite using only less than 1% amount of data compared
to current state-of-the-art method, GenPoCCL captures structural features of
the human body more effectively, surpassing existing methods in a variety of
human-centric perception tasks.

在本文中，我们通过实验证明了使用生成图像产生具有相同人体姿势的视觉上独特的图像，并提出了一种新颖的多正对比学习方法，该方法充分利用先前生成的图像来学习人体的结构特征。我们将整个学习过程称为 GenPoCCL。与当今最先进方法相比，GenPoCCL 仅使用不到 1% 的数据量，但更有效地捕捉到人体的结构特征，在多种以人为中心的感知任务中超过了现有方法。

多正同反学习与姿态一致生成图像

Multi Positive Contrastive Learning with Pose-Consistent Generated  Images

We investigate the potential of learning visual representations using
synthetic images generated by text-to-image models. This is a natural question
in the light of the excellent performance of such models in generating
high-quality images. We consider specifically the Stable Diffusion, one of the
leading open source text-to-image models. We show that (1) when the generative
model is configured with proper classifier-free guidance scale, training
self-supervised methods on synthetic images can match or beat the real image
counterpart; (2) by treating the multiple images generated from the same text
prompt as positives for each other, we develop a multi-positive contrastive
learning method, which we call StableRep. With solely synthetic images, the
representations learned by StableRep surpass the performance of representations
learned by SimCLR and CLIP using the same set of text prompts and corresponding
real images, on large scale datasets. When we further add language supervision,
StableRep trained with 20M synthetic images achieves better accuracy than CLIP
trained with 50M real images.

本研究探讨使用由文本到图像模型生成的合成图像学习视觉表示的潜力，提出一种多正对比学习方法，称为 StableRep。使用 20M 个合成图像训练的 StableRep 表现优于使用相同文本提示和对应真实图像的 SimCLR 和 CLIP 学习的表示，在大规模数据集上，加入语言监督之后， StableRep 的性能优于使用 50M 真实图像训练的 CLIP。