Multi Positive Contrastive Learning with Pose-Consistent Generated
  Images
Sho Inayoshi Aji Resindra Widya Satoshi Ozaki Junji Otsuka Takeshi Ohashi
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.