学习为视觉对应编写超列
Learning to Compose Hypercolumns for Visual Correspondence
摘要：
特征表示在视觉对应中起着至关重要的作用，最近的图像匹配方法采用深度堆叠的卷积层。然而，这些模型是整体的和静态的，因为它们通常使用特定级别的特征，例如，输出最后一层，并坚持与之不分的图像相匹配。在这项工作中，我们介绍了一种新的方法来视觉对应，动态地组成有效的功能，利用相关层的图像进行匹配。受目标检测中的多层特征组合和分类中的自适应推理架构的启发，所提出的方法被称为动态超像素流，通过从深度卷积神经网络中选择少量相关层来学习动态组合超列特征。我们证明了语义对应任务的有效性，即，在描绘相同对象或场景类别的不同实例的图像之间建立对应关系。在标准基准上的实验表明，所提出的方法大大提高了匹配性能的最先进的自适应和有效的方式。
英文摘要：
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers. These models, however, are both monolithic and static in the sense that they typically use a specific level of features, e.g., the output of the last layer, and adhere to it regardless of the images to match. In this work, we introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match. Inspired by both multi-layer feature composition in object detection and adaptive inference architectures in classification, the proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network. We demonstrate the effectiveness on the task of semantic correspondence, i.e., establishing correspondences between images depicting different instances of the same object or scene category. Experiments on standard benchmarks show that the proposed method greatly improves matching performance over the state of the art in an adaptive and efficient manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
