Dense Semantic Contrast for Self-Supervised Visual Representation Learning

• URL: http://arxiv.org/abs/2109.07756v1
• Date: Thu, 16 Sep 2021 07:04:05 GMT
• Title: Dense Semantic Contrast for Self-Supervised Visual Representation Learning
• Authors: Xiaoni Li, Yu Zhou, Yifei Zhang, Aoting Zhang, Wei Wang, Ning Jiang, Haiying Wu, Weiping Wang
• Abstract summary: We present Dense Semantic Contrast (DSC) for modeling semantic category decision boundaries at a dense level. We propose a dense cross-image semantic contrastive learning framework for multi-granularity representation learning. Experimental results show that our DSC model outperforms state-of-the-art methods when transferring to downstream dense prediction tasks.
• Score: 12.636783522731392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised representation learning for visual pre-training has achieved remarkable success with sample (instance or pixel) discrimination and semantics discovery of instance, whereas there still exists a non-negligible gap between pre-trained model and downstream dense prediction tasks. Concretely, these downstream tasks require more accurate representation, in other words, the pixels from the same object must belong to a shared semantic category, which is lacking in the previous methods. In this work, we present Dense Semantic Contrast (DSC) for modeling semantic category decision boundaries at a dense level to meet the requirement of these tasks. Furthermore, we propose a dense cross-image semantic contrastive learning framework for multi-granularity representation learning. Specially, we explicitly explore the semantic structure of the dataset by mining relations among pixels from different perspectives. For intra-image relation modeling, we discover pixel neighbors from multiple views. And for inter-image relations, we enforce pixel representation from the same semantic class to be more similar than the representation from different classes in one mini-batch. Experimental results show that our DSC model outperforms state-of-the-art methods when transferring to downstream dense prediction tasks, including object detection, semantic segmentation, and instance segmentation. Code will be made available.

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