Explicit Representation Alignment for Multimodal Sentiment Analysis

• URL: http://arxiv.org/abs/2606.09148v1
• Date: Mon, 08 Jun 2026 07:43:52 GMT
• Title: Explicit Representation Alignment for Multimodal Sentiment Analysis
• Abstract summary: Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images.<n>We identify representation misalignment between independently pretrained modality encoders as a key bottleneck for effective multimodal learning.<n>We propose a unified multimodal affective analysis framework that leverages vision-language models (VLMs) to convert visual content into structured textual descriptions.
• Score: 17.25685888421759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images. However, multimodal models often fail to consistently outperform strong text-only baselines, with performance varying significantly across fusion strategies. In this work, we identify representation misalignment between independently pretrained modality encoders as a key bottleneck for effective multimodal learning, and show through controlled experiments that alignment prior to fusion is often more important than fusion complexity. To address this issue, we propose a unified multimodal affective analysis framework that leverages vision-language models (VLMs) to convert visual content into structured textual descriptions, projecting heterogeneous modalities into a shared linguistic space and enabling interpretable text-centric reasoning. To further improve robustness, we introduce a hybrid learning strategy that combines semantic token selection with a batch-level uniformity regularization objective, encouraging a more dispersed and stable global feature space while mitigating noise introduced by VLM-generated descriptions. Experiments on multiple multimodal sentiment and emotion benchmarks show that our method consistently outperforms strong unimodal and multimodal baselines, achieving state-of-the-art performance. Our analysis further highlights the critical role of representation alignment in multimodal affective learning.

Related papers

• UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception [54.53657134205492]
  UniAlignment is a unified multimodal generation framework within a single diffusion transformer.<n>It incorporates both intrinsic-modal semantic alignment and cross-modal semantic alignment, thereby enhancing the model's cross-modal consistency and instruction-following robustness.<n>We present SemGen-Bench, a new benchmark specifically designed to evaluate multimodal semantic consistency under complex textual instructions.
  arXiv  Detail & Related papers  (2025-09-28T09:11:30Z)
• Explaining multimodal LLMs via intra-modal token interactions [55.27436637894534]
  Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse vision-language tasks, yet their internal decision-making mechanisms remain insufficiently understood.<n>We propose enhancing interpretability by leveraging intra-modal interaction.
  arXiv  Detail & Related papers  (2025-09-26T14:39:13Z)
• Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing [10.66971486730557]
  Multimodal Large Language Models (MLLMs) have shown substantial capabilities in integrating visual and textual information, yet frequently rely on spurious correlations.<n>This paper addresses the critical challenge of superficial correlation bias in MLLMs through a novel causal mediation-based debiasing framework.
  arXiv  Detail & Related papers  (2025-09-18T19:01:11Z)
• Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing [19.177541719713666]
  Multi-modal affective computing aims to automatically recognize and interpret human attitudes from diverse data sources such as images and text.<n>We propose a novel approach for affective computing that explicitly deconstructs visual and textual representations into shared (modality-invariant) and modality-specific components.
  arXiv  Detail & Related papers  (2025-06-08T11:15:57Z)
• Asymmetric Reinforcing against Multi-modal Representation Bias [59.685072206359855]
  We propose an Asymmetric Reinforcing method against Multimodal representation bias (ARM)<n>Our ARM dynamically reinforces the weak modalities while maintaining the ability to represent dominant modalities through conditional mutual information.<n>We have significantly improved the performance of multimodal learning, making notable progress in mitigating imbalanced multimodal learning.
  arXiv  Detail & Related papers  (2025-01-02T13:00:06Z)
• Hierarchical Banzhaf Interaction for General Video-Language Representation Learning [60.44337740854767]
  Multimodal representation learning plays an important role in the artificial intelligence domain.<n>We introduce a new approach that models video-text as game players using multivariate cooperative game theory.<n>We extend our original structure into a flexible encoder-decoder framework, enabling the model to adapt to various downstream tasks.
  arXiv  Detail & Related papers  (2024-12-30T14:09:15Z)
• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations [19.731611716111566]
  We propose a Multimodal fusion approach for learning modality-Exclusive and modality-Agnostic representations. We introduce a predictive self-attention module to capture reliable context dynamics within modalities. A hierarchical cross-modal attention module is designed to explore valuable element correlations among modalities. A double-discriminator strategy is presented to ensure the production of distinct representations in an adversarial manner.
  arXiv  Detail & Related papers  (2024-07-06T04:36:48Z)
• TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis [26.867610944625337]
  Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment by leveraging language, visual, and acoustic modalities.<n>Past research predominantly focused on improving representation learning techniques and feature fusion strategies.<n>We introduce a Text-oriented Cross-Attention Network (TCAN) emphasizing the predominant role of the text modality in MSA.
  arXiv  Detail & Related papers  (2024-04-06T07:56:09Z)
• WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge [73.76722241704488]
  We propose a plug-in framework named WisdoM to leverage the contextual world knowledge induced from the large vision-language models (LVLMs) for enhanced multimodal sentiment analysis. We show that our approach has substantial improvements over several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-12T16:08:07Z)
• Unified Discrete Diffusion for Simultaneous Vision-Language Generation [78.21352271140472]
  We present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.
  arXiv  Detail & Related papers  (2022-11-27T14:46:01Z)

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.