用多天气场景合成数据提升分割模型跨域泛化能力
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances
- 构建多天气条件下的合成城市场景数据集，实现像素级精准标注
- 通过对比学习强制不同天气下特征一致性，提升跨域适应性
- 为合成数据生成提供体积与多样性平衡的新思路，适合视觉算法研究者
本文提出一种新型合成数据集，涵盖多种天气条件下的城市场景，提供像素级精确、真实标签对齐的图像，以促进跨域特征对齐。同时，提出一种利用每场景多版本图像进行领域自适应与泛化的方法，强制不同天气条件下特征的一致性。实验结果表明，该数据集显著提升了多个对齐指标上的性能，解决了分割任务中领域自适应与泛化的关键挑战。研究还探讨了合成数据生成中的关键问题，如优化生成图像的数量与多样性之间的平衡，以增强分割表现。本工作为合成数据生成与领域自适应建立了新范式。
原文摘要 · Abstract (English)
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.
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