Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning

Shaofeng Zhang, Feng Zhu, Rui Zhao, Junchi Yan

Keywords: self supervised learning, contrastive learning

TL;DR: We propose a new self-supervised leanring method to learn both spatial-sensitive and global-discriminative information

Abstract: We propose ADCLR: \underline{A}ccurate and \underline{D}ense \underline{C}ontrastive \underline{R}epresentation \underline{L}earning, a novel self-supervised learning framework for learning accurate and dense vision representation. To extract spatial-sensitive information, ADCLR introduces query patches for contrasting in addition with global contrasting. Compared with previous dense contrasting methods, ADCLR mainly enjoys three merits: i) achieving both global-discriminative and spatial-sensitive representation, ii) model-efficient (no extra parameters in addition to the global contrasting baseline), and iii) correspondence-free and thus simpler to implement. Our approach achieves new state-of-the-art performance for contrastive methods. On classification tasks, for ViT-S, ADCLR achieves 78.1\% top-1 accuracy on ImageNet with linear probing, outperforming our baseline (DINO) without our devised techniques as plug-in, by 1.1\%. For ViT-B, ADCLR achieves 79.8\%, 84.0\% accuracy on ImageNet by linear probing and finetune, outperforming DINO by 0.6\%, 0.4\% accuracy. For dense tasks, on MS-COCO, ADCLR achieves significant improvements of 44.3\% AP on object detection, 39.7\% AP on instance segmentation, outperforming previous SOTA method SelfPatch by 2.2\% and 1.2\%, respectively. On ADE20K, ADCLR outperforms SelfPatch by 1.0\% mIoU, 1.2\% mAcc on the segmentation task.

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