  Self-supervised visual representation learning traditionally focuses on
image-level instance discrimination. Our study introduces an innovative,
fine-grained dimension by integrating patch-level discrimination into these
methodologies. This integration allows for the simultaneous analysis of local
and global visual features, thereby enriching the quality of the learned
representations. Initially, the original images undergo spatial augmentation.
Subsequently, we employ a distinctive photometric patch-level augmentation,
where each patch is individually augmented, independent from other patches
within the same view. This approach generates a diverse training dataset with
distinct color variations in each segment. The augmented images are then
processed through a self-distillation learning framework, utilizing the Vision
Transformer (ViT) as its backbone. The proposed method minimizes the
representation distances across both image and patch levels to capture details
from macro to micro perspectives. To this end, we present a simple yet
effective patch-matching algorithm to find the corresponding patches across the
augmented views. Thanks to the efficient structure of the patch-matching
algorithm, our method reduces computational complexity compared to similar
approaches. Consequently, we achieve an advanced understanding of the model
without adding significant computational requirements. We have extensively
pretrained our method on datasets of varied scales, such as Cifar10,
ImageNet-100, and ImageNet-1K. It demonstrates superior performance over
state-of-the-art self-supervised representation learning methods in image
classification and downstream tasks, such as copy detection and image
retrieval. The implementation of our method is accessible on GitHub.
