Semantic segmentation models have reached remarkable performance across various tasks. However, this performance is achieved with extremely large models, using powerful computational resources and without considering training and inference time. Real-world applications, on the other hand, necessitate models with minimal memory demands, efficient inference speed, and executable with low-resources embedded devices, such as self-driving vehicles. In this paper, we look at the challenge of real-time semantic segmentation across domains, and we train a model to act appropriately on real-world data even though it was trained on a synthetic realm. We employ a new lightweight and shallow discriminator that was specifically created for this purpose. To the best of our knowledge, we are the first to present a real-time adversarial approach for assessing the domain adaption problem in semantic segmentation. We tested our framework in the two standard protocol: GTA5 to Cityscapes and SYNTHIA to Cityscapes. Code is available at: https://github.com/taveraantonio/RTDA.

翻译：语义分解模型在各种任务中取得了显著的成绩。然而,这种表现是通过极其庞大的模型取得的,使用的是强大的计算资源,没有考虑到培训和推断时间。另一方面,现实世界应用需要最小记忆要求、高效推断速度和低资源嵌入装置(如自驾驶车辆)执行的模型。我们在本文件中审视了跨领域实时语义分解的挑战,我们培训了一个模型,以便在现实世界数据上采取适当行动,即使数据是在合成领域受过训练。我们使用的是专门为此目的创建的新的轻量和浅度歧视器。我们最了解的是,我们首先提出了实时对抗方法,用以评估语义分解中的域适应问题。我们在两个标准协议中测试了我们的框架:GTA5到城市景区和SYNTHIA到城市景区。代码见:https://github.com/taveraantonio/RTDA。