Graph matching (GM) aims at discovering node matching between graphs, by maximizing
the node-and edge-wise affinities between the matched elements. As an NP-hard problem …

Video captioning has become a broad and interesting research area. Attention-based
encoder-decoder methods are extensively used for caption generation. However, these …

Learning based feature matching methods have been commonly studied in recent years.
The core issue for learning feature matching is to how to learn (1) discriminative …

Feature matching is a fundamental task in the field of image processing, aimed at ensuring
correct correspondence between two sets of features. Putative matches constructed based …

Current point cloud registration methods are mainly based on local geometric information
and usually ignore the semantic information contained in the scenes. In this article, we treat …

Deriving from image matching and understanding, semantic keypoint matching aims at
establishing correspondence between keypoint sets in images. As graphs are powerful tools …

To achieve greater accuracy hypergraph matching algorithms require exponential increases
in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) …

Multiple object tracking (MOT) constitutes a critical research area within the field of computer
vision. The creation of robust and efficient systems, which can approximate the mechanisms …

Existing graph matching methods typically assume that there are similar structures between
graphs and they are matchable. However, these assumptions do not align with real-world …

Deep graph matching techniques have shown promising results in recent years. In this work,
we cast deep graph matching as a contrastive learning task and introduce a new objective …