A review of multimodal image matching: Methods and applications
Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …
similar structure/content from two or more images that are of significant modalities or …
Image matching from handcrafted to deep features: A survey
As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …
then correspond the same or similar structure/content from two or more images. Over the …
Deep neural network fusion via graph matching with applications to model ensemble and federated learning
Abstract Model fusion without accessing training data in machine learning has attracted
increasing interest due to the practical resource-saving and data privacy issues. During the …
increasing interest due to the practical resource-saving and data privacy issues. During the …
Neural graph matching network: Learning lawler's quadratic assignment problem with extension to hypergraph and multiple-graph matching
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix,
which can be generally formulated as Lawler's quadratic assignment problem (QAP). This …
which can be generally formulated as Lawler's quadratic assignment problem (QAP). This …
Deep model fusion: A survey
Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …
predictions of multiple deep learning models into a single one. It combines the abilities of …
Unsupervised learning of graph matching with mixture of modes via discrepancy minimization
Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard
nature. Recently (deep) learning-based approaches have shown their superiority over the …
nature. Recently (deep) learning-based approaches have shown their superiority over the …
Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning
This paper considers the setting of jointly matching and clustering multiple graphs belonging
to different groups, which naturally rises in many realistic problems. Both graph matching …
to different groups, which naturally rises in many realistic problems. Both graph matching …
Video super-resolution based on a spatio-temporal matching network
Deep spatio-temporal neural networks have shown promising performance for video super-
resolution (VSR) in recent years. However, most of them heavily rely on accuracy motion …
resolution (VSR) in recent years. However, most of them heavily rely on accuracy motion …
Learning for mismatch removal via graph attention networks
Recovering camera pose from two-view images is a critical problem in photogrammetry and
computer vision. For complex scenarios, point correspondences that are constructed by off …
computer vision. For complex scenarios, point correspondences that are constructed by off …
Mixsatgen: Learning graph mixing for sat instance generation
The Boolean satisfiability problem (SAT) stands as a canonical NP-complete task. In
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …