{"paper":{"title":"Learning Relative Representations for Fine-Grained Multimodal Alignment with Limited Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Relative representations via learnable anchors align token-level structures across modalities using only limited paired examples.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Shiwon Kim, Yu Rang Park","submitted_at":"2026-05-16T06:33:38Z","abstract_excerpt":"Multimodal pre-training demonstrates strong generalization performance, but this paradigm is often impractical in domains where paired data are scarce. A promising alternative is post-hoc multimodal alignment, which aligns separately pre-trained unimodal encoders using a limited number of paired examples. However, existing methods focus primarily on aligning global representations, missing patch-token relations. This may hinder transfer to tasks that require fine-grained cross-modal matching beyond coarse sample-level semantics. To address this issue, we propose a post-hoc alignment method tha"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Despite learning only the anchors without heavy projection layers, our approach consistently outperforms existing methods in zero-shot classification, cross-modal retrieval, and zero-shot segmentation by a substantial margin.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That training a set of learnable anchors to induce consistent cross-modal similarity patterns for matched pairs is sufficient to capture fine-grained token-level relations without requiring additional projection layers or larger paired datasets.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new post-hoc alignment technique uses learnable anchors to capture token-level relative similarities 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