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arXiv · 2207.04398

Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation

Ashraful Islam·Ben Lundell·Harpreet Sawhney·Sudipta Sinha·Peter Morales·Richard J. Radke
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Abstract

We present a self-supervised learning (SSL) method suitable for semi-global tasks such as object detection and semantic segmentation. We enforce local consistency between self-learned features, representing corresponding image locations of transformed versions of the same image, by minimizing a pixel-level local contrastive (LC) loss during training. LC-loss can be added to existing self-supervised learning methods with minimal overhead. We evaluate our SSL approach on two downstream tasks -- object detection and semantic segmentation, using COCO, PASCAL VOC, and CityScapes datasets. Our method outperforms the existing state-of-the-art SSL approaches by 1.9% on COCO object detection, 1.4% on PASCAL VOC detection, and 0.6% on CityScapes segmentation.

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Ashraful Islam, Ben Lundell, Harpreet Sawhney, Sudipta Sinha, Peter Morales, Richard J. Radke. 2022-07-10. Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation. https://arxiv.org/abs/2207.04398

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