Seminar: Pixel Club
FastJAM: a Fast Joint Alignment Model for Images
Date:
May,05,2026
Start Time:
11:30 - 12:30
Location:
506, Zisapel Building
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Lecturer:
Omri Hirsch
Research Areas:
| Joint Alignment (JA) aims to align a collection of images into a shared coordinate frame such that semantically corresponding features coincide spatially. Despite its importance in many vision applications, existing JA methods often rely on heavy optimization pipelines, large-capacity models, and extensive hyperparameter tuning, leading to long training times and limited scalability.
This talk presents FastJAM, a fast and lightweight joint alignment framework that reframes JA as a graph-based learning problem over sparse keypoints. FastJAM leverages pairwise correspondences from an off-the-shelf matcher and a graph neural network to efficiently predict per-image homography transformations, achieving state-of-the-art alignment quality while reducing runtime from minutes or hours to just seconds. Link to Paper: https://bgu-cs-vil.github.io/FastJAM/ |
| Omri Hirsch is an MSc. student in Computer Science at Ben-Gurion University of the Negev, conducting research in Computer Vision and Machine Learning in The Vision, Inference, and Learning (VIL) group under the supervision of Prof. Oren Freifeld. His research focuses on efficient geometric learning and joint image alignment, and he is the first author of FastJAM, recently accepted to NeurIPS 2025. He has previously worked on medical imaging and computational pathology in collaboration with Dr. Yonatan Winetraub’s lab at Stanford University, as well as on underwater computer vision and color restoration under Dr. Derya Akkaynak. Omri is a recipient of competitive scholarships for outstanding MSc. students in AI and Data Science for two consecutive years, and was awarded NeurIPS 2025 financial support in recognition of his research potential.
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