# Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances

Source: [Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances](<https://www.sota2.com/research/paper/68fa765c1e209131cea6c9fa>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:52:28.667Z

Authors: Javier Montalvo, Roberto Alcover-Couso, Pablo Carballeira, \\'Alvaro Garc\\'ia-Mart\\'in, Juan C. SanMiguel, Marcos Escudero-Vi\\~nolo

Published: 2024-12-21T11:50:07.000Z

## Abstract

This paper introduces a novel synthetic dataset that captures urban scenes under a variety of weather conditions, providing pixel-perfect, ground-truth-aligned images to facilitate effective feature alignment across domains. Additionally, we propose a method for domain adaptation and generalization that takes advantage of the multiple versions of each scene, enforcing feature consistency across different weather scenarios. Our experimental results demonstrate the impact of our dataset in improving performance across several alignment metrics, addressing key challenges in domain adaptation and generalization for segmentation tasks. This research also explores critical aspects of synthetic data generation, such as optimizing the balance between the volume and variability of generated images to enhance segmentation performance. Ultimately, this work sets forth a new paradigm for synthetic data generation and domain adaptation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.16592>)

[PDF](<https://arxiv.org/pdf/2412.16592.pdf>)