论文标题
无监督的域适应性用于语义分割,使用潜在表示混合使用一击图像到图像翻译
Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing
论文作者
论文摘要
域的适应是处理这两种域转移的重要策略之一,在大规模土地使用/土地覆盖地图计算中广泛遇到,以及像素级地面真理的稀缺性对于监督语义细分至关重要。通常通过生成的对抗网络重新使用源源域样本来重点介绍对抗域的适应性,但报告的成功水平有所不同,但它们遭受了语义上的不一致,视觉腐败的困扰,并且通常需要大量目标域样本。在这封信中,我们提出了一种针对非常高分辨率图像的语义分割的新的无监督域的适应方法,i)i)导致语义一致且无噪声的图像,ii)以单个目标域样本(即一弹射)和III的单个目标域(即一小部分)在“状态”中所需的参数数量。更具体地说,是根据编码器编码器原理提出的图像到图像翻译范式,其中潜在的内容表示跨域混合在一起,并进一步引入了感知网络模块和损耗函数以实施语义一致性。跨城市比较实验表明,所提出的方法优于最先进的域适应方法。我们的源代码将在\ url {https://github.com/sarmadfismael/lrm_i2i}提供。
Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks, have reported varying levels of success, yet they suffer from semantic inconsistencies, visual corruptions, and often require a large number of target domain samples. In this letter, we propose a new unsupervised domain adaptation method for the semantic segmentation of very high resolution images, that i) leads to semantically consistent and noise-free images, ii) operates with a single target domain sample (i.e. one-shot) and iii) at a fraction of the number of parameters required from state-of-the-art methods. More specifically an image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains, and a perceptual network module and loss function is further introduced to enforce semantic consistency. Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods. Our source code will be available at \url{https://github.com/Sarmadfismael/LRM_I2I}.