{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spacejam-a-lightweight-and-regularization","title":"SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images","arxiv_id":"2407.11850","date":"2024-07-16","proceeding":null,"authors":["Nir Barel","Ron Shapira Weber","Nir Mualem","Shahaf E. Finder","Oren Freifeld"],"abstract":"The unsupervised task of Joint Alignment (JA) of images is beset by challenges such as high complexity, geometric distortions, and convergence to poor local or even global optima. Although Vision Transformers (ViT) have recently provided valuable features for JA, they fall short of fully addressing these issues. Consequently, researchers frequently depend on expensive models and numerous regularization terms, resulting in long training times and challenging hyperparameter tuning. We introduce the Spatial Joint Alignment Model (SpaceJAM), a novel approach that addresses the JA task with efficiency and simplicity. SpaceJAM leverages a compact architecture with only 16K trainable parameters and uniquely operates without the need for regularization or atlas maintenance. Evaluations on SPair-71K and CUB datasets demonstrate that SpaceJAM matches the alignment capabilities of existing methods while significantly reducing computational demands and achieving at least a 10x speedup. SpaceJAM sets a new standard for rapid and effective image alignment, making the process more accessible and efficient. Our code is available at: https://bgu-cs-vil.github.io/SpaceJAM/.","url_abs":"https://arxiv.org/abs/2407.11850v2","url_pdf":"https://arxiv.org/pdf/2407.11850v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spacejam-a-lightweight-and-regularization","repo_url":"https://github.com/BGU-CS-VIL/SpaceJAM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"16k","task_name":"16k"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.11850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11850"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/BGU-CS-VIL/SpaceJAM","reach":null}],"summary":{"ran":1,"unverified":2},"by_repo_kind":{"official":{"samples":3,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"76adf50fcc21bfe6","entry":"Transformer","repo":"BGU-CS-VIL/SpaceJAM","repo_kind":"official","path":"spacejam/models/transformers/sequence_transformer.py","file_url":"https://github.com/BGU-CS-VIL/SpaceJAM/blob/HEAD/spacejam/models/transformers/sequence_transformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"76adf50fcc21bfe6"}},{"code_sha256_prefix":"8bd46f3b50fcdeb2","entry":"HomographyTransformer","repo":"BGU-CS-VIL/SpaceJAM","repo_kind":"official","path":"spacejam/models/transformers/sequence_transformer.py","file_url":"https://github.com/BGU-CS-VIL/SpaceJAM/blob/HEAD/spacejam/models/transformers/sequence_transformer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8bd46f3b50fcdeb2"}},{"code_sha256_prefix":"5b5f3c848798d32c","entry":"SequenceTransformer","repo":"BGU-CS-VIL/SpaceJAM","repo_kind":"official","path":"spacejam/models/transformers/sequence_transformer.py","file_url":"https://github.com/BGU-CS-VIL/SpaceJAM/blob/HEAD/spacejam/models/transformers/sequence_transformer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5b5f3c848798d32c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}