Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training. Additionally, such models do not generalise well to environments where the statistical properties of data do not perfectly match those of training scenes, and this can be a significant problem for intelligent vehicles. Hence, domain adaptation approaches have been introduced to transfer knowledge acquired on a label-abundant source domain to a related label-scarce target domain. In this work, we design and carefully analyse multiple latent space-shaping regularisation strategies that work together to reduce the domain shift. More in detail, we devise a feature clustering strategy to increase domain alignment, a feature perpendicularity constraint to space apart features belonging to different semantic classes, including those not present in the current batch, and a feature norm alignment strategy to separate active and inactive channels. In addition, we propose a novel evaluation metric to capture the relative performance of an adapted model with respect to supervised training. We validate our framework in driving scenarios, considering both synthetic-to-real and real-to-real adaptation, outperforming previous feature-level state-of-the-art methods on multiple road scenes benchmarks.

翻译：深层学习模型在了解道路场景方面获得了令人印象深刻的准确性,然而,它们需要大量贴标签的样本来进行培训。此外,这些模型没有很好地概括出数据统计特性与培训场景不完全匹配的环境,这对智能车辆来说可能是一个重大问题。因此,引入了领域适应办法,将从标签丰度源域获得的知识转移到相关的标签碎片目标域。在这项工作中,我们设计并仔细分析多种潜在的空间整形战略,共同减少域变。更详细地说,我们设计了一个特征组合战略,以加强域对属于不同语义类(包括不在当前批次的语义类)的空间分离特性的特性限制,一个特性规范调整战略,将活跃和非活跃的渠道分开。此外,我们提出一个新的评价指标,以了解经调整的模式在监督培训方面的相对性表现。我们验证了我们的驱动情景框架,同时考虑到合成到现实和真实到现实的适应,超过了多个路景场的先前地貌水平。