论文标题
共同学习特征提取和语义通信的成本汇总
Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence
论文作者
论文摘要
在语义上相似的图像之间建立密集的对应关系是由于重要的阶层内变化和背景临时，这是具有挑战性的任务之一。为了解决这些问题，已经提出了许多方法，专注于学习特征提取器或成本汇总，从而产生了次优性能。在本文中，我们提出了一个新的框架，用于共同学习特征提取和语义对应的成本汇总。通过从每个模块中利用伪标签，以功能提取和成本聚合模块组成的网络以增强方式同时学习。此外，要忽略不可靠的伪标签，我们为以弱监督的方式学习网络提供了一种信心感知的对比损失函数。我们在语义对应的标准基准上展示了我们的竞争结果。
Establishing dense correspondences across semantically similar images is one of the challenging tasks due to the significant intra-class variations and background clutters. To solve these problems, numerous methods have been proposed, focused on learning feature extractor or cost aggregation independently, which yields sub-optimal performance. In this paper, we propose a novel framework for jointly learning feature extraction and cost aggregation for semantic correspondence. By exploiting the pseudo labels from each module, the networks consisting of feature extraction and cost aggregation modules are simultaneously learned in a boosting fashion. Moreover, to ignore unreliable pseudo labels, we present a confidence-aware contrastive loss function for learning the networks in a weakly-supervised manner. We demonstrate our competitive results on standard benchmarks for semantic correspondence.
