English(EN) A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation

新框架利用扩散模型为城市语义分割生成低成本数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一个新颖的框架，该框架利用扩散模型为城市语义分割生成高保真、领域对齐的图像。该方法使用不完美的伪标签来使现成的扩散模型适应特定目标领域，例如 Cityscapes。该系统过滤掉次优生成，纠正图像-标签错位，并标准化语义，将弱合成数据转化为有效的训练集。实验表明，分割性能显著提高，快速构建的合成数据集在与需要大量手动设计的相比时具有竞争力。 AI

影响能够为城市场景理解大规模、高质量地创建训练数据，有可能加速自动驾驶和城市规划领域的发展。

排序理由该集群包含一篇详细介绍新数据生成框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Damjan Kal\v{s}an, Denis Zavadski, Tim K\"uchler, Haebom Lee, Stefan Roth, Carsten Rother · 2026-08-28 04:00

一种用于城市语义分割的低成本训练数据生成框架

arXiv:2510.11567v2 Announce Type: replace-cross Abstract: Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real imagery. This gap is particularly pronounced when adapting to a specific ta…

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一种用于城市语义分割的低成本训练数据生成框架

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