  Establishing semantic correspondence is a core problem in computer vision and
remains challenging due to large intra-class variations and lack of annotated
data. In this paper, we aim to incorporate global semantic context in a
flexible manner to overcome the limitations of prior work that relies on local
semantic representations. To this end, we first propose a context-aware
semantic representation that incorporates spatial layout for robust matching
against local ambiguities. We then develop a novel dynamic fusion strategy
based on attention mechanism to weave the advantages of both local and context
features by integrating semantic cues from multiple scales. We instantiate our
strategy by designing an end-to-end learnable deep network, named as Dynamic
Context Correspondence Network (DCCNet). To train the network, we adopt a
multi-auxiliary task loss to improve the efficiency of our weakly-supervised
learning procedure. Our approach achieves superior or competitive performance
over previous methods on several challenging datasets, including PF-Pascal,
PF-Willow, and TSS, demonstrating its effectiveness and generality.
