Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training

• URL: http://arxiv.org/abs/2509.17816v1
• Date: Mon, 22 Sep 2025 14:11:02 GMT
• Title: Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training
• Authors: Brown Ebouky, Ajad Chhatkuli, Cristiano Malossi, Christoph Studer, Roy Assaf, Andrea Bartezzaghi,
• Abstract summary: Self-supervised learning (SSL) has emerged as a central paradigm for training foundation models.<n>We propose GLARE, a novel continual self-supervised pre-training task designed to enhance downstream segmentation performance.
• Score: 11.897717409259492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) has emerged as a central paradigm for training foundation models by leveraging large-scale unlabeled datasets, often producing representations with strong generalization capabilities. These models are typically pre-trained on general-purpose datasets such as ImageNet and subsequently adapted to various downstream tasks through finetuning. While recent advances have explored parameter-efficient strategies for adapting pre-trained models, extending SSL pre-training itself to new domains - particularly under limited data regimes and for dense prediction tasks - remains underexplored. In this work, we address the problem of adapting vision foundation models to new domains in an unsupervised and data-efficient manner, specifically targeting downstream semantic segmentation. We propose GLARE (Global Local and Regional Enforcement), a novel continual self-supervised pre-training task designed to enhance downstream segmentation performance. GLARE introduces patch-level augmentations to encourage local consistency and incorporates a regional consistency constraint that leverages spatial semantics in the data. For efficient continual pre-training, we initialize Vision Transformers (ViTs) with weights from existing SSL models and update only lightweight adapter modules - specifically UniAdapter - while keeping the rest of the backbone frozen. Experiments across multiple semantic segmentation benchmarks on different domains demonstrate that GLARE consistently improves downstream performance with minimal computational and parameter overhead.

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