论文标题
探索图像到图像翻译任务中的对比度学习的贴片语义关系
Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks
论文作者
论文摘要
最近，已经提出了基于对比的基于学习的图像翻译方法，该方法与不同的空间位置对比以增强空间对应关系。但是，这些方法通常忽略图像中的各种语义关系。为了解决这个问题，在这里，我们提出了一种新颖的语义关系一致性（SRC）正则化以及脱钩的对比学习，该学习通过着重于单个图像的图像贴片之间的异质语义来利用各种语义。为了进一步提高性能，我们通过利用语义关系来提出坚硬的负面开采。我们验证了三个任务的方法：单模式和多模式图像翻译，以及用于图像翻译的GAN压缩任务。实验结果证实了我们方法在所有三个任务中的最新性能。
Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the methods often ignore the diverse semantic relation within the images. To address this, here we propose a novel semantic relation consistency (SRC) regularization along with the decoupled contrastive learning, which utilize the diverse semantics by focusing on the heterogeneous semantics between the image patches of a single image. To further improve the performance, we present a hard negative mining by exploiting the semantic relation. We verified our method for three tasks: single-modal and multi-modal image translations, and GAN compression task for image translation. Experimental results confirmed the state-of-art performance of our method in all the three tasks.
