FastJAM: a Fast Joint Alignment Model for Images

2025-10-26

AI Summary

To address the issues of excessive training time, model bloat, and hyperparameter sensitivity in joint image alignment (JA), this paper proposes a lightweight and efficient graph neural network (GNN) approach. Methodologically, it constructs a keypoint correspondence graph using off-the-shelf matchers, integrates fast nonparametric clustering with GNN-based relational propagation, and introduces an inverse-composition loss—eliminating the need for regularization terms or hyperparameter tuning. Homography parameters are directly regressed via image-level pooling. The key contribution is the first end-to-end graph learning paradigm tailored for JA, achieving state-of-the-art alignment accuracy across multiple benchmarks. Computation time is reduced from minutes or hours to mere seconds, while maintaining high robustness and practical applicability.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Joint Alignment (JA) of images aims to align a collection of images into a unified coordinate frame, such that semantically-similar features appear at corresponding spatial locations. Most existing approaches often require long training times, large-capacity models, and extensive hyperparameter tuning. We introduce FastJAM, a rapid, graph-based method that drastically reduces the computational complexity of joint alignment tasks. FastJAM leverages pairwise matches computed by an off-the-shelf image matcher, together with a rapid nonparametric clustering, to construct a graph representing intra- and inter-image keypoint relations. A graph neural network propagates and aggregates these correspondences, efficiently predicting per-image homography parameters via image-level pooling. Utilizing an inverse-compositional loss, that eliminates the need for a regularization term over the predicted transformations (and thus also obviates the hyperparameter tuning associated with such terms), FastJAM performs image JA quickly and effectively. Experimental results on several benchmarks demonstrate that FastJAM achieves results better than existing modern JA methods in terms of alignment quality, while reducing computation time from hours or minutes to mere seconds. Our code is available at our project webpage, https://bgu-cs-vil.github.io/FastJAM/

Problem

• FastJAM rapidly aligns image collections into unified coordinate frames
• It reduces computational complexity using graph-based methods and pairwise matches
• The method eliminates hyperparameter tuning while improving alignment quality

Innovation

• Graph-based method reduces joint alignment complexity
• Leverages pairwise matches and nonparametric clustering
• Graph neural network predicts homography parameters efficiently