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Self-supervised visual representation learning traditionally focuses onimage-level instance discrimination. Our study introduces an innovativedimension by integrating patch-level discrimination into these methodologies.This integration allows for the simultaneous analysis of both local and globalvisual features, thereby enriching the quality of the representations learned.Initially, the original images undergo spatial augmentation. Subsequently, weemploy a distinctive photometric patch-level augmentation, where each patch isindividually augmented, independent from other patches within the same view.This approach generates a diverse training dataset with distinct colorvariations in each segment. The augmented images are then processed through aself-distillation learning framework, utilizing the Vision Transformer (ViT) asits backbone. The proposed method minimizes the representation distances acrossboth image and patch levels to capture details from macro to microperspectives. To this end, we present a simple yet effective patch-matchingalgorithm that can find the corresponding patches across the augmented views.Thanks to the efficient structure of the patch-matching algorithm, our methodreduces computational complexity compared to similar approaches. Consequently,we achieve an advanced understanding of the model without adding significantcomputational requirements. We have extensively pre-trained our method ondatasets of varied scales, such as Cifar10, ImageNet-100, and ImageNet-1K. Itdemonstrates superior performance over state-of-the-art self-supervisedrepresentation learning methods in image classification and downstream tasks,such as copy detection and image retrieval. The implementation of our method isaccessible on GitHub.

PW-Self: Patch-Wise Self-Supervised Visual Representation Learning