Patch-level Representation Learning for Self-supervised Vision Transformers

By Sukmin Yun, Hankook Lee, Jaehyung Kim, Jinwoo Shin. 2022

89 citations recorded by the source corpus.

Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance further by utilizing the architectural advan-tages of the underlying neural network, as the current state-of-the-art visual pretext tasks for SSL do not enjoy the ben-efit, i.e., they are architecture-agnostic. In particular, we fo-cus on Vision Transformers (ViTs), which have gained much attention recently as a better architectural choice, often out-performing convolutional networks for various visual tasks. The unique characteristic of ViT is that it takes a sequence of disjoint patches from an image and processes patch-level representations internally. Inspired by this, we design a simple yet effective visual pretext task, coined Self Patch, for learning better patch-level representations. To be specific, we enforce invariance against each patch and its neigh-bors, i.e., each patch treats similar neighboring patches as positive samples. Consequently, training ViTs with Self-Patch learns more semantically meaningful relations among patches (without using human-annotated labels), which can be beneficial, in particular, to downstream tasks of a dense prediction type. Despite its simplicity, we demonstrate that it can significantly improve the performance of existing SSL methods for various visual tasks, including object detection and semantic segmentation. Specifically, Self Patch signif-icantly improves the recent self-supervised ViT, DINO, by achieving +1.3 AP on COCO object detection, +1.2 AP on COCO instance segmentation, and +2.9 mIoU on ADE20K semantic segmentation.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “Recently, referring to CV in general, several innovations have been proposed [53], [54], reviewed in many surveys [24], [55], [56], [57], [58].”
A Cookbook of Self-Supervised Learning (2023, background): “Unlike popular contrastive objectives which encourage augmented views of an image to produce similar features, SelfPatch encourages adjacent patches within a single image to produce similar features [Yun et al., 2022].”