@inproceedings{
dai2024trga,
title={TrGa: Reconsidering the Application of Graph Neural Networks in Two-View Correspondence Pruning},
author={Luanyuan Dai and Xiaoyu Du and Jinhui Tang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=c9I9NAbkpz}
}
Two-view correspondence pruning aims to accurately remove incorrect correspondences (outliers) from  initial ones. Graph Neural Networks (GNNs) incorporated by Multilayer Perceptrons (MLPs) are treated as a powerful manner to handle sparse and unevenly distributed data. 
However, the expression capability of correspondence features obtained by MLPs is limited by their inherent insufficient of context information.
In addition, previous works directly utilize the outputs of off-the-shelf GNNs, thus leading to confusion between  sparse correspondence attribute features and their global structural information. 
To alleviate these issues, we propose a two-view correspondence pruning network TrGa. Specifically, we firstly use complete Transformer structures instead of context-agnostic MLPs to capture correspondence features with global context information and stronger  expression capability. After that, we introduce the Concatenation Graph Node and Global Structure  (CGNS) block to separately capture the interaction patterns among sparse correspondence attribute features and the global structural information among them, which can prevent their confusion. Finally, the proposed Feature Dimension Transformation and Enhancement (FDTE) block is applied for dimension transformation and feature augmentation. Additionally, we propose an efficient variant C-TrGa, in which the similarity matrix of the proposed  C-Transformer  is computed along the channel dimension. Extensive experiments demonstrate that the proposed TrGa and C-TrGa outperform state-of-the-art methods in different computer vision tasks.  The code is provided in the supplementary materials.
TrGa: Reconsidering the Application of Graph Neural Networks in Two-View Correspondence Pruning
Luanyuan Dai Xiaoyu Du Jinhui Tang