104,787 research outputs found
Learning Combinatorial Embedding Networks for Deep Graph Matching
Graph matching refers to finding node correspondence between graphs, such
that the corresponding node and edge's affinity can be maximized. In addition
with its NP-completeness nature, another important challenge is effective
modeling of the node-wise and structure-wise affinity across graphs and the
resulting objective, to guide the matching procedure effectively finding the
true matching against noises. To this end, this paper devises an end-to-end
differentiable deep network pipeline to learn the affinity for graph matching.
It involves a supervised permutation loss regarding with node correspondence to
capture the combinatorial nature for graph matching. Meanwhile deep graph
embedding models are adopted to parameterize both intra-graph and cross-graph
affinity functions, instead of the traditional shallow and simple parametric
forms e.g. a Gaussian kernel. The embedding can also effectively capture the
higher-order structure beyond second-order edges. The permutation loss model is
agnostic to the number of nodes, and the embedding model is shared among nodes
such that the network allows for varying numbers of nodes in graphs for
training and inference. Moreover, our network is class-agnostic with some
generalization capability across different categories. All these features are
welcomed for real-world applications. Experiments show its superiority against
state-of-the-art graph matching learning methods.Comment: ICCV2019 oral. Code available at
https://github.com/Thinklab-SJTU/PCA-G
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Building on recent progress at the intersection of combinatorial optimization
and deep learning, we propose an end-to-end trainable architecture for deep
graph matching that contains unmodified combinatorial solvers. Using the
presence of heavily optimized combinatorial solvers together with some
improvements in architecture design, we advance state-of-the-art on deep graph
matching benchmarks for keypoint correspondence. In addition, we highlight the
conceptual advantages of incorporating solvers into deep learning
architectures, such as the possibility of post-processing with a strong
multi-graph matching solver or the indifference to changes in the training
setting. Finally, we propose two new challenging experimental setups. The code
is available at https://github.com/martius-lab/blackbox-deep-graph-matchingComment: ECCV 2020 conference pape
Deep Reinforcement Learning of Graph Matching
Graph matching (GM) under node and pairwise constraints has been a building
block in areas from combinatorial optimization, data mining to computer vision,
for effective structural representation and association. We present a
reinforcement learning solver for GM i.e. RGM that seeks the node
correspondence between pairwise graphs, whereby the node embedding model on the
association graph is learned to sequentially find the node-to-node matching.
Our method differs from the previous deep graph matching model in the sense
that they are focused on the front-end feature extraction and affinity function
learning, while our method aims to learn the back-end decision making given the
affinity objective function whether obtained by learning or not. Such an
objective function maximization setting naturally fits with the reinforcement
learning mechanism, of which the learning procedure is label-free. These
features make it more suitable for practical usage. Extensive experimental
results on both synthetic datasets, Willow Object dataset, Pascal VOC dataset,
and QAPLIB showcase superior performance regarding both matching accuracy and
efficiency. To our best knowledge, this is the first deep reinforcement
learning solver for graph matching
- …