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

• URL: http://arxiv.org/abs/2207.04398v1
• Date: Sun, 10 Jul 2022 06:53:15 GMT
• Title: Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation
• Authors: Ashraful Islam, Ben Lundell, Harpreet Sawhney, Sudipta Sinha, Peter Morales, Richard J. Radke
• Abstract summary: 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. LC-loss can be added to existing self-supervised learning methods with minimal overhead. 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.
• Score: 9.711659088922838
• License: http://creativecommons.org/licenses/by/4.0/
• 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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