To Align or Not to Align: Strategic Multimodal Representation Alignment for Optimal Performance

• URL: http://arxiv.org/abs/2511.12121v3
• Date: Wed, 19 Nov 2025 02:26:02 GMT
• Title: To Align or Not to Align: Strategic Multimodal Representation Alignment for Optimal Performance
• Authors: Wanlong Fang, Tianle Zhang, Alvin Chan,
• Abstract summary: We introduce a controllable contrastive learning module that enables precise manipulation of alignment strength during training.<n>Our results show that the impact of explicit alignment on the performance of unimodal models is related to the characteristics of the data.<n>We identify an optimal alignment strength that balances modality-specific signals and shared redundancy in the mixed information distributions.
• Score: 13.520604242963897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal learning often relies on aligning representations across modalities to enable effective information integration, an approach traditionally assumed to be universally beneficial. However, prior research has primarily taken an observational approach, examining naturally occurring alignment in multimodal data and exploring its correlation with model performance, without systematically studying the direct effects of explicitly enforced alignment between representations of different modalities. In this work, we investigate how explicit alignment influences both model performance and representation alignment under different modality-specific information structures. Specifically, we introduce a controllable contrastive learning module that enables precise manipulation of alignment strength during training, allowing us to explore when explicit alignment improves or hinders performance. Our results on synthetic and real datasets under different data characteristics show that the impact of explicit alignment on the performance of unimodal models is related to the characteristics of the data: the optimal level of alignment depends on the amount of redundancy between the different modalities. We identify an optimal alignment strength that balances modality-specific signals and shared redundancy in the mixed information distributions. This work provides practical guidance on when and how explicit alignment should be applied to achieve optimal unimodal encoder performance.

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)
• From Coefficients to Directions: Rethinking Model Merging with Directional Alignment [66.99062575537555]
  We introduce a unified geometric framework, emphMerging with Directional Alignment (method), which aligns directional structures consistently in both the parameter and feature spaces.<n>Our analysis shows that directional alignment improves structural coherence, and extensive experiments across benchmarks, model scales, and task configurations further validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2025-11-29T08:40:58Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• How does the optimizer implicitly bias the model merging loss landscape? [66.96572894292895]
  We show that a single quantity -- the effective noise scale -- unifies the impact of inference and data choices on model merging.<n>Across datasets, the effectiveness of merging success is a non-monotonic function of effective noise, with a distinct optimum.
  arXiv  Detail & Related papers  (2025-10-06T10:56:41Z)
• Unified modality separation: A vision-language framework for unsupervised domain adaptation [60.8391821117794]
  Unsupervised domain adaptation (UDA) enables models trained on a labeled source domain to handle new unlabeled domains.<n>We propose a unified modality separation framework that accommodates both modality-specific and modality-invariant components.<n>Our methods achieve up to 9% performance gain with 9 times of computational efficiencies.
  arXiv  Detail & Related papers  (2025-08-07T02:51:10Z)
• IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignment [29.703775936837012]
  Large Language Models (LLMs) have achieved impressive performance through Supervised Fine-tuning (SFT) on diverse instructional datasets.<n>When training on multiple capabilities simultaneously, the mixture training dataset, governed by volumes of data from different domains, is a critical factor that directly impacts the final model's performance.<n>We introduce an innovative data equilibrium framework designed to effectively optimize volumes of data from different domains within mixture SFT datasets.
  arXiv  Detail & Related papers  (2025-05-19T06:42:44Z)
• Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence [83.15764564701706]
  We propose a novel framework that performs vision-language alignment by integrating Cauchy-Schwarz divergence with mutual information.<n>We find that the CS divergence seamlessly addresses the InfoNCE's alignment-uniformity conflict and serves complementary roles with InfoNCE.<n> Experiments on text-to-image generation and cross-modality retrieval tasks demonstrate the effectiveness of our method on vision-language alignment.
  arXiv  Detail & Related papers  (2025-02-24T10:29:15Z)
• Understanding the Emergence of Multimodal Representation Alignment [22.81361409729974]
  A recent line of work has found that independently trained unimodal models of increasing scale and performance can become implicitly aligned with each other.<n>We show that both the emergence of alignment and its relationship with task performance depend on several critical data characteristics.<n>Our findings suggest that alignment may not be universally beneficial; rather, its impact on performance varies depending on the dataset and task.
  arXiv  Detail & Related papers  (2025-02-22T16:27:31Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Triple Disentangled Representation Learning for Multimodal Affective Analysis [20.37986194570143]
  Multimodal learning has exhibited a significant advantage in affective analysis tasks. Many emerging studies focus on disentangling the modality-invariant and modality-specific representations from input data and then fusing them for prediction. We propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data.
  arXiv  Detail & Related papers  (2024-01-29T12:45:27Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)

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.