… Here, we propose a fully-differentiable graph matching procedure which aims to reach a
data-driven neighborhood consensus between matched node pairs without the need to solve …

… On the one hand, recently proposed deep feature matchers may have obvious limitations in
… On the other hand, these deep methods inherently include a strategy of mismatch removal …

… accuracy [25], which inspired us to consider quadratic structural constraint in deep graph
matching to maximize the adjacency consensus and achieve global consistency. More precisely…

… In this sense, the construction of graph topology is only a pre-processing step, … the graph
topology for matching can hinder the capacity of deep GM frameworks. For a pre-defined graph …

… graph convolutional network to jointly learn entity and relation embeddings based on the
triadic graph by a … and relations by computing a graph-level matching consensus. In the second …

… techniques to perturb the graph structure and degrade the quality of deep graph matching:
(1… method that reaches a data-driven neighborhood consensus between matched node pairs. …

… i) We develop a novel supervised deep network based pipeline for graph matching, whereby
the objective involves the permutation loss based on a Sinkhorn net rather than structured …

… and deep learning, we propose an end-to-end trainable architecture for deep graph matching
… -of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, …

… In this paper, we present a new feature matching method, which formulates the matching of
… advanced consensus of neighborhood topology, which can better exploit the consensus of …

… deep learning. To delve into multi-modal data comprehending, this paper focuses on addressing
the problem of image-text matching … 42, 48], and scene graph generation [5]. Specifically…