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SPARC: SuperPixel-Aware Region Contrastive Learning for Self-Supervised Dense Prediction

Summary

arXiv paper SPARC introduces a region-level contrastive learning framework that uses superpixels to align augmented views, jointly optimizing region-level and global image-level objectives. It reports gains up to +9.79 mIoU for semantic segmentation and +4.88 AP for object detection over MoCo-v2 and DenseCL.

SourcearXiv Computer VisionAuthor: David Szczecina, Yuanpei Xiang, Jitao Hu, David Clausi, Yuhao Chen, Jason Deglint, Paul Fieguth
SPARC: SuperPixel-Aware Region Contrastive Learning for Self-Supervised Dense Prediction
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[Submitted on 16 Sep 2026]

Title:SPARC: SuperPixel-Aware Region Contrastive Learning for Self-Supervised Dense Prediction

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Abstract:Self-supervised learning (SSL) has become an effective approach for learning visual representations without manual annotations. Among SSL approaches, contrastive learning has been widely used for visual representation learning. However, existing contrastive SSL methods have focused primarily on image-level or pixel-level representation learning, while region-level representation learning remains less explored. We propose SPARC, a region-level contrastive learning framework that leverages superpixels to establish explicit correspondence between augmented image views. SPARC introduces a region contrastive branch that performs superpixel-based feature pooling and optimizes a region-level contrastive objective jointly with a global image-level objective. Under identical settings, SPARC consistently outperforms previous methods such as MoCo-v2 and DenseCL, achieving improvements of up to +9.79 mIoU for semantic segmentation and +4.88 AP for object detection. Ablation studies further demonstrate that region-level objectives produce the strongest performance. Thus, region-level contrastive learning is an effective approach for improving self-supervised visual pretraining for dense prediction tasks. Code repository can be accessed at this https URL.

Comments: 5 pages, 2 figures. Submitted to the IEEE ICASSP 2027 Conference

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

MSC classes: 68T05

ACM classes: I.2.6

Cite as: arXiv:2609.25067 [cs.CV]

(or arXiv:2609.25067v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.25067

arXiv-issued DOI via DataCite

Submission history

From: David Szczecina [view email] [v1] Wed, 16 Sep 2026 22:40:07 UTC (176 KB)

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Key points and analysis

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Key points

  • SPARC uses superpixels to build explicit region-level correspondences between augmented image views, addressing the limited exploration of region-level contrastive self-supervised learning.
  • The framework adds a region contrastive branch that pools superpixel features and jointly optimizes a region-level objective with a global image-level objective.
  • Under identical settings, it outperforms MoCo-v2 and DenseCL by up to +9.79 mIoU on semantic segmentation and +4.88 AP on object detection.
  • Ablations show region-level objectives deliver the strongest gains; code is available on GitHub.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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