  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.
