Improving Dense Contrastive Learning with Dense Negative Pairs

• URL: http://arxiv.org/abs/2210.05063v1
• Date: Tue, 11 Oct 2022 00:26:59 GMT
• Title: Improving Dense Contrastive Learning with Dense Negative Pairs
• Authors: Berk Iskender, Zhenlin Xu, Simon Kornblith, Enhung Chu, Maryam Khademi
• Abstract summary: We study how to improve the quality of the representations learned by DenseCL by modifying the training scheme and objective function. Our results show 3.5% and 4% mAP improvement over SimCLR and DenseCL in COCO multi-label classification.
• Score: 15.728417548134047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many contrastive representation learning methods learn a single global representation of an entire image. However, dense contrastive representation learning methods such as DenseCL [19] can learn better representations for tasks requiring stronger spatial localization of features, such as multi-label classification, detection, and segmentation. In this work, we study how to improve the quality of the representations learned by DenseCL by modifying the training scheme and objective function, and propose DenseCL++. We also conduct several ablation studies to better understand the effects of: (i) various techniques to form dense negative pairs among augmentations of different images, (ii) cross-view dense negative and positive pairs, and (iii) an auxiliary reconstruction task. Our results show 3.5% and 4% mAP improvement over SimCLR [3] and DenseCL in COCO multi-label classification. In COCO and VOC segmentation tasks, we achieve 1.8% and 0.7% mIoU improvements over SimCLR, respectively.

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