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 …
Deep graph similarity learning: A survey
In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …
graphs is considered a key problem, which can further facilitate various learning tasks, such …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
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 …
High-order information matters: Learning relation and topology for occluded person re-identification
Occluded person re-identification (ReID) aims to match occluded person images to holistic
ones across dis-joint cameras. In this paper, we propose a novel framework by learning high …
ones across dis-joint cameras. In this paper, we propose a novel framework by learning high …
Robust point cloud registration framework based on deep graph matching
Abstract 3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made great …
robotics. Recently, learning-based point cloud registration methods have made great …
Weisfeiler and leman go machine learning: The story so far
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …
Deep graph matching consensus
This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …
correspondences between graphs. First, we use localized node embeddings computed by a …
Learnable graph matching: Incorporating graph partitioning with deep feature learning for multiple object tracking
Data association across frames is at the core of Multiple Object Tracking (MOT) task. This
problem is usually solved by a traditional graph-based optimization or directly learned via …
problem is usually solved by a traditional graph-based optimization or directly learned via …
Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking
Data association-based multiple object tracking (MOT) involves multiple separated modules
processed or optimized differently, which results in complex method design and requires …
processed or optimized differently, which results in complex method design and requires …