Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?

• URL: http://arxiv.org/abs/2505.24211v1
• Date: Fri, 30 May 2025 04:51:54 GMT
• Title: Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?
• Authors: Jiwan Chung, Janghan Yoon, Junhyeong Park, Sangeyl Lee, Joowon Yang, Sooyeon Park, Youngjae Yu,
• Abstract summary: We introduce ACON, a dataset of 1,000 images paired with captions, editing instructions, and Q&A pairs to evaluate cross-modal transfers.<n>Our experiments reveal that any-to-any models do not consistently demonstrate greater cross-modal consistency than specialized models in pointwise evaluations.
• Score: 14.044169097789034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Any-to-any generative models aim to enable seamless interpretation and generation across multiple modalities within a unified framework, yet their ability to preserve relationships across modalities remains uncertain. Do unified models truly achieve cross-modal coherence, or is this coherence merely perceived? To explore this, we introduce ACON, a dataset of 1,000 images (500 newly contributed) paired with captions, editing instructions, and Q&A pairs to evaluate cross-modal transfers rigorously. Using three consistency criteria-cyclic consistency, forward equivariance, and conjugated equivariance-our experiments reveal that any-to-any models do not consistently demonstrate greater cross-modal consistency than specialized models in pointwise evaluations such as cyclic consistency. However, equivariance evaluations uncover weak but observable consistency through structured analyses of the intermediate latent space enabled by multiple editing operations. We release our code and data at https://github.com/JiwanChung/ACON.

Related papers

• Orthogonalized Multimodal Contrastive Learning with Asymmetric Masking for Structured Representations [4.67724003380452]
  Multimodal learning seeks to integrate information from heterogeneous sources, where signals may be shared across modalities, specific to individual modalities, or emerge only through their interaction.<n>While self-supervised multimodal contrastive learning has achieved remarkable progress, most existing methods predominantly capture redundant cross-modal signals, often neglecting modality-specific (unique) and interaction-driven (synergistic) information.<n>Recent extensions broaden this perspective, yet they either fail to explicitly model synergistic interactions or learn different information components in an entangled manner, leading to incomplete representations and potential information leakage.<n>We introduce textbfCOrAL, a principled framework
  arXiv  Detail & Related papers  (2026-02-16T18:06:53Z)
• Mixture-of-Models: Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation [0.0]
  This paper introduces the N-Way Self-Evaluating Deliberation (NSED) protocol, a Mixture-of-Models (MoM) architecture that constructs emergent composite models from a plurality of distinct expert agents.<n>Unlike traditional Mixture-of-Experts (MoE) which rely on static gating networks, NSED employs a Dynamic Expertise Broker that treats model selection as a variation of the Knapsack Problem.
  arXiv  Detail & Related papers  (2026-01-23T16:11:54Z)
• CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolution [20.823419395675412]
  CrossCheck-Bench is a diagnostic benchmark for evaluating contradiction detection in multimodal inputs.<n>We evaluate 13 state-of-the-art vision-language models and observe a consistent performance drop as tasks shift from perceptual matching to logical contradiction detection.
  arXiv  Detail & Related papers  (2025-11-19T12:17:15Z)
• Harnessing Consistency for Robust Test-Time LLM Ensemble [88.55393815158608]
  CoRE is a plug-and-play technique that harnesses model consistency for robust LLM ensemble.<n> Token-level consistency captures fine-grained disagreements by applying a low-pass filter to downweight uncertain tokens.<n>Model-level consistency models global agreement by promoting model outputs with high self-confidence.
  arXiv  Detail & Related papers  (2025-10-12T04:18:45Z)
• Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation [49.98025799046136]
  We introduce Merge-And-GuidE, a two-stage framework that leverages model merging for guided decoding.<n>In Stage 1, MAGE resolves a compatibility problem between the guidance and base models.<n>In Stage 2, we merge explicit and implicit value models into a unified guidance proxy, which then steers the decoding of the base model from Stage 1.
  arXiv  Detail & Related papers  (2025-10-04T11:10:07Z)
• Test-Time Consistency in Vision Language Models [26.475993408532304]
  Vision-Language Models (VLMs) have achieved impressive performance across a wide range of multimodal tasks.<n>Recent benchmarks, such as MM-R3, highlight that even state-of-the-art VLMs can produce divergent predictions across semantically equivalent inputs.<n>We propose a simple and effective test-time consistency framework that enhances semantic consistency without supervised re-training.
  arXiv  Detail & Related papers  (2025-06-27T17:09:44Z)
• Multi-Level Collaboration in Model Merging [56.31088116526825]
  This paper explores the intrinsic connections between model merging and model ensembling.<n>We find that even when previous restrictions are not met, there is still a way for model merging to attain a near-identical and superior performance similar to that of ensembling.
  arXiv  Detail & Related papers  (2025-03-03T07:45:04Z)
• Bridging the inference gap in Mutimodal Variational Autoencoders [6.246098300155483]
  Multimodal Variational Autoencoders offer versatile and scalable methods for generating unobserved modalities from observed ones.<n>Recent models using mixturesof-experts aggregation suffer from theoretically grounded limitations that restrict their generation quality on complex datasets.<n>We propose a novel interpretable model able to learn both joint and conditional distributions without introducing mixture aggregation.
  arXiv  Detail & Related papers  (2025-02-06T10:43:55Z)
• Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery [62.43562856605473]
  We argue for the computational advantages of a recurrent architecture with complex-valued weights. We propose a fully convolutional autoencoder, SynCx, that performs iterative constraint satisfaction.
  arXiv  Detail & Related papers  (2024-05-27T15:47:03Z)
• Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion [54.33764537135906]
  VideoQA Transformer models demonstrate competitive performance on standard benchmarks. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Are they achieving high scores by exploiting biases and spurious features?
  arXiv  Detail & Related papers  (2023-06-15T06:45:46Z)
• Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition [69.32305810128994]
  Incongruity between modalities poses a challenge for multimodal fusion, especially in affect recognition. We propose the Hierarchical Crossmodal Transformer with Dynamic Modality Gating (HCT-DMG), a lightweight incongruity-aware model. HCT-DMG: 1) outperforms previous multimodal models with a reduced size of approximately 0.8M parameters; 2) recognizes hard samples where incongruity makes affect recognition difficult; 3) mitigates the incongruity at the latent level in crossmodal attention.
  arXiv  Detail & Related papers  (2023-05-23T01:24:15Z)
• BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency [66.8685113725007]
  BiCro aims to estimate soft labels for noisy data pairs to reflect their true correspondence degree. experiments on three popular cross-modal matching datasets demonstrate that BiCro significantly improves the noise-robustness of various matching models.
  arXiv  Detail & Related papers  (2023-03-22T09:33:50Z)
• Syntactically Robust Training on Partially-Observed Data for Open Information Extraction [25.59133746149343]
  Open Information Extraction models have shown promising results with sufficient supervision. We propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution.
  arXiv  Detail & Related papers  (2023-01-17T12:39:13Z)
• Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set [50.67431815647126]
  Post-hoc global/local feature attribution methods are being progressively employed to understand machine learning models. We show that partial orders of local/global feature importance arise from this methodology. We show that every relation among features present in these partial orders also holds in the rankings provided by existing approaches.
  arXiv  Detail & Related papers  (2021-10-26T02:53:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.