pith. sign in

arxiv: 2605.16834 · v1 · pith:VDK6MBX5new · submitted 2026-05-16 · 💻 cs.CV · cs.AI· cs.LG

Learning Relative Representations for Fine-Grained Multimodal Alignment with Limited Data

Shiwon Kim , Yu Rang Park This is my paper

Pith reviewed 2026-05-19 20:45 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords relative representationsmultimodal alignmentpost-hoc alignmentfine-grained alignmentzero-shot classificationcross-modal retrievallearnable anchorslimited paired data
0
0 comments X

The pith

Relative representations via learnable anchors align token-level structures across modalities using only limited paired examples.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that separately pre-trained image and text encoders can be aligned after the fact for fine-grained tasks by representing each token through its similarities to a shared set of learnable anchors rather than by projecting global features. A sympathetic reader would care because many practical domains offer only scarce paired data, so full joint pre-training is often impossible, yet tasks like precise segmentation still need matching at the patch or word level instead of whole-sample semantics. The anchors are trained so that matched image-text pairs produce similar patterns of token-to-anchor similarities in both spaces. If this works, the method delivers stronger zero-shot classification, retrieval, and segmentation without adding large projection networks or collecting more pairs.

Core claim

Representing images and texts through their token-level similarities to a set of learnable anchors in each modality space, and training those anchors to induce consistent cross-modal similarity patterns for matched pairs, captures fine-grained structure and yields better transfer to zero-shot classification, cross-modal retrieval, and zero-shot segmentation than prior global-alignment methods, all while using only the anchors and a small number of paired examples.

What carries the argument

Learnable anchors that turn each token into a vector of similarities to the anchors, with the anchors optimized so matched image-text pairs show matching similarity vectors across modalities.

If this is right

  • Zero-shot classification improves because token-level relations transfer to new classes without additional training.
  • Cross-modal retrieval accuracy rises as the model matches at the level of individual patches and words rather than whole samples.
  • Zero-shot segmentation benefits directly from the preserved fine-grained structure between image regions and text tokens.
  • Effective alignment remains possible even when the number of paired training examples is small.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same anchor consistency mechanism could be tested on aligning additional modalities such as audio with video by defining anchors in each new space.
  • If the approach scales, it might allow alignment pipelines to start from even smaller paired sets than those used in the reported experiments.
  • One could check whether freezing the anchors after training still preserves performance on downstream tasks that require token-level matching.

Load-bearing premise

Training anchors to make similarity patterns consistent for matched pairs is sufficient to capture the fine-grained token-level relations needed for alignment.

What would settle it

Measure whether the learned similarity patterns actually recover known token correspondences on a dataset with ground-truth fine-grained matches; if gains over global baselines vanish when those patterns do not align with the true matches, the claim is falsified.

Figures

Figures reproduced from arXiv: 2605.16834 by Shiwon Kim, Yu Rang Park.

Figure 1
Figure 1. Figure 1: Performance of PAL and post-hoc alignment [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of PAL. (a) Frozen token embeddings are first converted into token-to-anchor similarity [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative analysis of anchor specialization and reuse. Rows illustrate anchor specialization within the [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of the number of anchors K. Dashed lines indicate the strongest baseline for each task, and annotations indicate the number of trainable anchor parameters. pairs using hard overlap and Dice coefficient [31]. In all cases, matched pairs show substantially higher consistency than mismatched pairs. This indicates that the learned anchors are not activated independently within each modality, but instead… view at source ↗
read the original abstract

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 that learns token-level cross-modal structure using relative representations. Specifically, we represent images and texts through their token-level similarities to a set of learnable anchors in each modality space, which are trained to induce consistent cross-modal similarity patterns for matched pairs. 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. This highlights the importance of modeling fine-grained cross-modal structure for effective post-hoc multimodal alignment with limited paired data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a post-hoc multimodal alignment method for scenarios with limited paired data. Instead of learning heavy projection layers, it introduces learnable anchors in each modality's token space and represents every token by its similarity vector to these anchors. The anchors are optimized via a consistency loss so that the resulting similarity patterns match between paired image-text examples. The authors report that this relative-representation approach yields substantial gains over prior post-hoc methods on zero-shot classification, cross-modal retrieval, and zero-shot segmentation.

Significance. If the empirical gains are robust and the method demonstrably preserves token-level correspondences rather than collapsing to global alignment, the work would provide a lightweight, data-efficient route to fine-grained multimodal alignment. This could be particularly useful in specialized domains where large paired corpora are unavailable.

major comments (2)
  1. [§3] §3: The consistency loss is defined solely on matched pairs using token-to-anchor similarities. Nothing in the objective explicitly penalizes collapse of per-token variation; anchors could therefore converge to modality-level statistics that produce near-constant similarity vectors within each sample. This directly threatens the central claim that the approach encodes fine-grained token-token relations.
  2. [Experimental results] Experimental results (zero-shot segmentation): the reported improvements would be more convincing if accompanied by an ablation that replaces the token-to-anchor representation with a global (pooled) variant and shows a clear drop in segmentation metrics. Without such a control, it remains possible that gains stem from better global alignment rather than the claimed fine-grained structure.
minor comments (2)
  1. The abstract states 'substantial margin' without numerical values; adding concrete deltas (e.g., +X% on retrieval) would improve immediate readability.
  2. [§3] Notation for the anchor set and the similarity computation should be introduced with an explicit equation at the start of §3 to aid readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below, indicating where revisions have been made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3] §3: The consistency loss is defined solely on matched pairs using token-to-anchor similarities. Nothing in the objective explicitly penalizes collapse of per-token variation; anchors could therefore converge to modality-level statistics that produce near-constant similarity vectors within each sample. This directly threatens the central claim that the approach encodes fine-grained token-token relations.

    Authors: We appreciate the referee highlighting this potential limitation of the objective. While the loss operates on matched pairs, the multi-anchor formulation is designed to capture diverse relative patterns per token rather than a single global statistic. To directly address the collapse concern, we have added a discussion in the revised §3 along with an empirical analysis of per-token variance in the learned similarity vectors (new Figure S1 in the supplement), which shows that the vectors retain substantial variation across tokens within each sample and do not converge to constants. revision: partial

  2. Referee: [Experimental results] Experimental results (zero-shot segmentation): the reported improvements would be more convincing if accompanied by an ablation that replaces the token-to-anchor representation with a global (pooled) variant and shows a clear drop in segmentation metrics. Without such a control, it remains possible that gains stem from better global alignment rather than the claimed fine-grained structure.

    Authors: We agree that this ablation would provide clearer evidence for the role of token-level structure. We have therefore implemented a global variant that replaces the per-token similarity vectors with a single pooled representation (or equivalently a single anchor) and re-evaluated it on the zero-shot segmentation benchmarks. The results, now reported in the revised experimental section and Table 4, show a consistent drop in mIoU relative to the full token-to-anchor method, supporting that the gains arise from fine-grained rather than purely global alignment. revision: yes

Circularity Check

0 steps flagged

No circularity: relative representations defined via independent anchor training

full rationale

The paper introduces learnable anchors whose similarities define the token-level representations, with anchors optimized via a consistency loss on matched pairs. This construction is explicit and self-contained: the downstream zero-shot tasks are evaluated empirically after training, rather than being algebraically equivalent to the fitted anchors or prior self-citations. No uniqueness theorems, ansatzes smuggled via citation, or fitted-input predictions are present in the abstract or described method. The approach adds new trainable components and reports performance margins, keeping the derivation chain non-tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The method rests on the domain assumption that separately pre-trained unimodal encoders contain sufficient structure for post-hoc fine-grained alignment via anchors, plus the ad-hoc choice of learnable anchors as the primary trainable component.

axioms (1)
  • domain assumption Separately pre-trained unimodal encoders can be aligned post-hoc using limited paired examples to capture fine-grained relations.
    Stated as the promising alternative to full multimodal pre-training in the abstract.
invented entities (1)
  • learnable anchors in each modality space no independent evidence
    purpose: To represent token-level similarities and induce consistent cross-modal similarity patterns for matched pairs.
    Introduced as the core mechanism for learning relative representations without heavy projection layers.

pith-pipeline@v0.9.0 · 5706 in / 1386 out tokens · 36937 ms · 2026-05-19T20:45:06.663156+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages · 2 internal anchors

  1. [1]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. InProceed- ings of the 38th International Conference on Machine Learning (ICML), pages 8748–8763. ...

    work page 2021

  2. [2]

    Le, Yun- Hsuan Sung, Zhen Li, and Tom Duerig

    Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V . Le, Yun- Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. InProceedings of the 38th International Conference on Machine Learning (ICML), pages 4904–4916. PMLR, 2021

    work page 2021

  3. [3]

    The platonic representation hypothesis

    Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis. InProceedings of the 41st International Conference on Machine Learning (ICML). PMLR, 2024

    work page 2024

  4. [4]

    Relative representations enable zero-shot latent space communication

    Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. Relative representations enable zero-shot latent space communication. In International Conference on Learning Representations (ICLR), 2023

    work page 2023

  5. [5]

    Linearly mapping from image to text space.arXiv preprint arXiv:2209.15162, 2022

    Jack Merullo, Louis Castricato, Carsten Eickhoff, and Ellie Pavlick. Linearly mapping from image to text space.arXiv preprint arXiv:2209.15162, 2022

    work page arXiv 2022

  6. [6]

    ASIF: Coupled data turns unimodal models to multimodal without training

    Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. ASIF: Coupled data turns unimodal models to multimodal without training. InAdvances in Neural Information Processing Systems (NeurIPS), volume 36, pages 15303–15319, 2023

    work page 2023

  7. [7]

    Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, and Maksims V olkovs

    Noël V ouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, and Maksims V olkovs. Data-efficient multimodal fusion on a single GPU. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    work page 2024

  8. [8]

    Chinchali, and Ufuk Topcu

    Po-han Li, Sandeep P. Chinchali, and Ufuk Topcu. CSA: Data-efficient mapping of unimodal features to multimodal features. InInternational Conference on Learning Representations (ICLR), 2025

    work page 2025

  9. [9]

    O’Connor

    Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Ankit Singh, and Noel E. O’Connor. Harnessing frozen unimodal encoders for flexible multi- modal alignment. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 29847–29857, 2025

    work page 2025

  10. [10]

    Assessing and learning alignment of unimodal vision and language models

    Le Zhang, Qian Yang, and Aishwarya Agrawal. Assessing and learning alignment of unimodal vision and language models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

    work page 2025

  11. [11]

    With limited data for multimodal alignment, let the STRUCTURE guide you

    Fabian Gröger, Shuo Wen, Huyen Le, and Maria Brbi ´c. With limited data for multimodal alignment, let the STRUCTURE guide you. InAdvances in Neural Information Processing Systems (NeurIPS), 2025

    work page 2025

  12. [12]

    FILIP: Fine-grained interactive language-image pre-training

    Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: Fine-grained interactive language-image pre-training. InInternational Conference on Learning Representations (ICLR), 2022

    work page 2022

  13. [13]

    Fine-grained late- interaction multi-modal retrieval for retrieval augmented visual question answering.Advances in Neural Information Processing Systems, 36:22820–22840, 2023

    Weizhe Lin, Jinghong Chen, Jingbiao Mei, Alexandru Coca, and Bill Byrne. Fine-grained late- interaction multi-modal retrieval for retrieval augmented visual question answering.Advances in Neural Information Processing Systems, 36:22820–22840, 2023

    work page 2023

  14. [14]

    Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, and Jovana Mitrovic

    Ioana Bica, Anastasija Ilic, Matthias Bauer, Goker Erdogan, Matko Bošnjak, Christos Kaplanis, Alexey A. Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, and Jovana Mitrovic. Improving fine-grained understanding in image-text pre-training. InProceedings of the 41st International Conference on Machine Learning (ICML). PMLR, 2024. 10

    work page 2024

  15. [15]

    Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation

    Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation. InInternational conference on machine learning, pages 12888–12900. PMLR, 2022

    work page 2022

  16. [16]

    CoCa: Contrastive Captioners are Image-Text Foundation Models

    Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models.arXiv preprint arXiv:2205.01917, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  17. [17]

    Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35: 23716–23736, 2022

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35: 23716–23736, 2022

    work page 2022

  18. [18]

    PaLI: A Jointly-Scaled Multilingual Language-Image Model

    Xi Chen, Xiao Wang, Soravit Changpinyo, Anthony J Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, et al. Pali: A jointly-scaled multilingual language-image model.arXiv preprint arXiv:2209.06794, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  19. [19]

    Lawrence Zitnick

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: Common objects in context. In European Conference on Computer Vision (ECCV), pages 740–755. Springer, 2014

    work page 2014

  20. [20]

    An analysis of single-layer networks in unsuper- vised feature learning

    Adam Coates, Andrew Ng, and Honglak Lee. An analysis of single-layer networks in unsuper- vised feature learning. InProceedings of the fourteenth international conference on artificial intelligence and statistics, pages 215–223. JMLR Workshop and Conference Proceedings, 2011

    work page 2011

  21. [21]

    Learning multiple layers of features from tiny images

    Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009

    work page 2009

  22. [22]

    Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

    Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In2004 conference on computer vision and pattern recognition workshop, pages 178–178. IEEE, 2004

    work page 2004

  23. [23]

    Describing textures in the wild

    Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 3606–3613, 2014

    work page 2014

  24. [24]

    Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7):2217–2226, 2019

    work page 2019

  25. [25]

    Plummer, Liwei Wang, Chris M

    Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models.International Journal of Computer Vision, 123(1):74–93, 2017

    work page 2017

  26. [26]

    Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisser- man. The PASCAL visual object classes (VOC) challenge.International Journal of Computer Vision, 88(2):303–338, 2010

    work page 2010

  27. [27]

    The role of context for object detection and semantic segmentation in the wild

    Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 891–898, 2014

    work page 2014

  28. [28]

    Scene parsing through ADE20K dataset

    Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ADE20K dataset. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 633–641, 2017

    work page 2017

  29. [29]

    Maskclip: Masked self-distillation ad- vances contrastive language-image pretraining

    Xiaoyi Dong, Jianmin Bao, Yinglin Zheng, Ting Zhang, Dongdong Chen, Hao Yang, Ming Zeng, Weiming Zhang, Lu Yuan, Dong Chen, et al. Maskclip: Masked self-distillation ad- vances contrastive language-image pretraining. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10995–11005, 2023. 11

    work page 2023

  30. [30]

    Canonical correlation analysis

    David Weenink. Canonical correlation analysis. InProceedings of the Institute of Phonetic Sciences of the University of Amsterdam, volume 25, pages 81–99. University of Amsterdam Amsterdam, 2003

    work page 2003

  31. [31]

    Measures of the amount of ecologic association between species.Ecology, 26(3): 297–302, 1945

    Lee R Dice. Measures of the amount of ecologic association between species.Ecology, 26(3): 297–302, 1945. 12

    work page 1945