TiCAL:Typicality-Based Consistency-Aware Learning for Multimodal Emotion Recognition

• URL: http://arxiv.org/abs/2511.15085v1
• Date: Wed, 19 Nov 2025 03:49:22 GMT
• Title: TiCAL:Typicality-Based Consistency-Aware Learning for Multimodal Emotion Recognition
• Authors: Wen Yin, Siyu Zhan, Cencen Liu, Xin Hu, Guiduo Duan, Xiurui Xie, Yuan-Fang Li, Tao He,
• Abstract summary: Multimodal Emotion Recognition aims to accurately identify human emotional states by integrating heterogeneous modalities such as visual, auditory, and textual data.<n>Existing approaches predominantly rely on unified emotion labels to supervise model training, often overlooking a critical challenge: inter-modal emotion conflicts.<n>We propose Typicality-based Consistent-aware Multimodal Emotion Recognition (TiCAL), inspired by the stage-wise nature of human emotion perception.
• Score: 31.4260327895046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Emotion Recognition (MER) aims to accurately identify human emotional states by integrating heterogeneous modalities such as visual, auditory, and textual data. Existing approaches predominantly rely on unified emotion labels to supervise model training, often overlooking a critical challenge: inter-modal emotion conflicts, wherein different modalities within the same sample may express divergent emotional tendencies. In this work, we address this overlooked issue by proposing a novel framework, Typicality-based Consistent-aware Multimodal Emotion Recognition (TiCAL), inspired by the stage-wise nature of human emotion perception. TiCAL dynamically assesses the consistency of each training sample by leveraging pseudo unimodal emotion labels alongside a typicality estimation. To further enhance emotion representation, we embed features in a hyperbolic space, enabling the capture of fine-grained distinctions among emotional categories. By incorporating consistency estimates into the learning process, our method improves model performance, particularly on samples exhibiting high modality inconsistency. Extensive experiments on benchmark datasets, e.g, CMU-MOSEI and MER2023, validate the effectiveness of TiCAL in mitigating inter-modal emotional conflicts and enhancing overall recognition accuracy, e.g., with about 2.6% improvements over the state-of-the-art DMD.

Related papers

• Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition [56.00118641432005]
  We propose a Memory-guided Prototypical Co-occurrence Learning framework that explicitly models emotion co-occurrence patterns.<n>Inspired by human cognitive memory systems, we introduce a memory retrieval strategy to extract semantic-level co-occurrence associations.<n>Our model learns affectively informative representations for accurate emotion distribution prediction.
  arXiv  Detail & Related papers  (2026-02-24T04:11:25Z)
• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics.<n>We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention.<n>Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks.<n>Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations [35.63053777817013]
  GatedxLSTM is a novel multimodal Emotion Recognition in Conversation (ERC) model.<n>It considers voice and transcripts of both the speaker and their conversational partner to identify the most influential sentences driving emotional shifts.<n>It achieves state-of-the-art (SOTA) performance among open-source methods in four-class emotion classification.
  arXiv  Detail & Related papers  (2025-03-26T18:46:18Z)
• MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis [53.012111671763776]
  This study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions. Results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy.
  arXiv  Detail & Related papers  (2024-11-18T02:09:48Z)
• Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition [23.505616142198487]
  We develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition. The model learns universal latent representations of EEG signals through pre-training on large scale dataset. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks.
  arXiv  Detail & Related papers  (2024-05-28T14:31:11Z)
• Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations [15.705757672984662]
  Multimodal Emotion Recognition in Conversations (MERC) is a significant development direction for machine intelligence. Many data in MERC naturally exhibit an imbalanced distribution of emotion categories, and researchers ignore the negative impact of imbalanced data on emotion recognition. We propose the Class Boundary Enhanced Representation Learning (CBERL) model to address the imbalanced distribution of emotion categories in raw data. We have conducted extensive experiments on the IEMOCAP and MELD benchmark datasets, and the results show that CBERL has achieved a certain performance improvement in the effectiveness of emotion recognition.
  arXiv  Detail & Related papers  (2023-12-11T12:35:17Z)
• Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition [28.881092401807894]
  In paralinguistic analysis for emotion detection from speech, emotions have been identified with discrete or dimensional (continuous-valued) labels. We propose a model to jointly predict continuous and discrete emotional attributes.
  arXiv  Detail & Related papers  (2022-10-29T16:12:31Z)
• Seeking Subjectivity in Visual Emotion Distribution Learning [93.96205258496697]
  Visual Emotion Analysis (VEA) aims to predict people's emotions towards different visual stimuli. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. We propose a novel textitSubjectivity Appraise-and-Match Network (SAMNet) to investigate the subjectivity in visual emotion distribution.
  arXiv  Detail & Related papers  (2022-07-25T02:20:03Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning [29.262204241732565]
  Existing methods assume that all emotions-of-interest are given a priori as annotated training examples. We conceptualize one-shot recognition of emotions in context -- a new problem aimed at recognizing human affect states in finer particle level from a single support sample. All variants of our model clearly outperform the random baseline, while leveraging the semantic scene context consistently improves the learnt representations.
  arXiv  Detail & Related papers  (2021-11-30T10:35:20Z)
• MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition [118.73025093045652]
  We propose a pre-training model textbfMEmoBERT for multimodal emotion recognition. Unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text prediction. Our proposed MEmoBERT significantly enhances emotion recognition performance.
  arXiv  Detail & Related papers  (2021-10-27T09:57:00Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)

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.