XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

• URL: http://arxiv.org/abs/2602.05496v1
• Date: Thu, 05 Feb 2026 09:58:41 GMT
• Title: XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning
• Authors: Hanwen Zhang, Yao Liu, Peiyuan Jiang, Lang Junjie, Xie Jun, Yihui He, Yajiao Deng, Siyu Du, Qiao Liu,
• Abstract summary: We introduce XEmoGPT, a novel EMER framework capable of both perceiving and reasoning over emotional cues.<n>We construct a large-scale dataset, EmoCue, designed to teach XEmoGPT how to reason over multimodal emotional cues.<n> Experimental results show that XEmoGPT achieves strong performance in both emotional cue perception and reasoning.
• Score: 7.204821736879453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable Multimodal Emotion Recognition plays a crucial role in applications such as human-computer interaction and social media analytics. However, current approaches struggle with cue-level perception and reasoning due to two main challenges: 1) general-purpose modality encoders are pretrained to capture global structures and general semantics rather than fine-grained emotional cues, resulting in limited sensitivity to emotional signals; and 2) available datasets usually involve a trade-off between annotation quality and scale, which leads to insufficient supervision for emotional cues and ultimately limits cue-level reasoning. Moreover, existing evaluation metrics are inadequate for assessing cue-level reasoning performance. To address these challenges, we propose eXplainable Emotion GPT (XEmoGPT), a novel EMER framework capable of both perceiving and reasoning over emotional cues. It incorporates two specialized modules: the Video Emotional Cue Bridge (VECB) and the Audio Emotional Cue Bridge (AECB), which enhance the video and audio encoders through carefully designed tasks for fine-grained emotional cue perception. To further support cue-level reasoning, we construct a large-scale dataset, EmoCue, designed to teach XEmoGPT how to reason over multimodal emotional cues. In addition, we introduce EmoCue-360, an automated metric that extracts and matches emotional cues using semantic similarity, and release EmoCue-Eval, a benchmark of 400 expert-annotated samples covering diverse emotional scenarios. Experimental results show that XEmoGPT achieves strong performance in both emotional cue perception and reasoning.

Related papers

• Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding [45.13650362585136]
  We present Emotion-LLaMAv2 and the MMEVerse benchmark, establishing an end-to-end pipeline together with a standardized evaluation setting for emotion recognition and reasoning.<n>An end-to-end multiview encoder eliminates external face detection and captures nuanced emotional cues via richer spatial and temporal multiview tokens.<n>A perception-to-cognition curriculum instruction tuning scheme within the LLaMA2 backbone unifies emotion recognition and free-form emotion reasoning.
  arXiv  Detail & Related papers  (2026-01-23T05:02:43Z)
• E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis [54.763420895859035]
  We present ELLM2-EEG-to-Emotion Large Language Model, first MLLM framework for interpretable emotion analysis from EEG.<n>ELLM integrates a pretrained EEG encoder with Q-based LLMs through learnable projection layers, employing a multi-stage training pipeline.<n>Experiments on the dataset across seven emotion categories demonstrate that ELLM2-EEG-to-Emotion Large Language Model achieves excellent performance on emotion classification.
  arXiv  Detail & Related papers  (2026-01-11T13:21:20Z)
• VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models [46.591026037722436]
  We propose a novel affective cues-guided reasoning framework that unifies fundamental attribute perception, expression analysis, and high-level emotional understanding.<n>At the core of our approach is a family of video emotion foundation models (VidEmo), specifically designed for emotion reasoning and instruction-following.<n>We establish a foundational data infrastructure and introduce a emotion-centric fine-grained dataset consisting of 2.1M diverse instruction-based samples.
  arXiv  Detail & Related papers  (2025-11-04T16:31:09Z)
• MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models [108.61337743051483]
  We present MME-Emotion, a systematic benchmark that assesses both emotional understanding and reasoning capabilities of MLLMs.<n>MME-Emotion contains over 6,000 curated video clips with task-specific questioning-answering (QA) pairs, spanning broad scenarios to formulate eight emotional tasks.<n>It incorporates a holistic evaluation suite with hybrid metrics for emotion recognition and reasoning, analyzed through a multi-agent system framework.
  arXiv  Detail & Related papers  (2025-08-11T03:14:55Z)
• KEVER^2: Knowledge-Enhanced Visual Emotion Reasoning and Retrieval [35.77379981826482]
  We propose textbfK-EVERtextsuperscript2, a knowledge-enhanced framework for emotion reasoning and retrieval.<n>Our approach introduces a semantically structured formulation of visual emotion cues and integrates external affective knowledge through multimodal alignment.<n>We validate our framework on three representative benchmarks, Emotion6, EmoSet, and M-Disaster, covering social media imagery, human-centric scenes, and disaster contexts.
  arXiv  Detail & Related papers  (2025-05-30T08:33:32Z)
• UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech [61.989360995528905]
  We propose UDDETTS, a universal framework unifying discrete and dimensional emotions for controllable emotional TTS.<n>This model introduces the interpretable Arousal-Dominance-Valence (ADV) space for dimensional emotion description and supports emotion control driven by either discrete emotion labels or nonlinearly quantified ADV values.<n>Experiments show that UDDETTS achieves linear emotion control along three interpretable dimensions, and exhibits superior end-to-end emotional speech synthesis capabilities.
  arXiv  Detail & Related papers  (2025-05-15T12:57:19Z)
• Emotion-Qwen: A Unified Framework for Emotion and Vision Understanding [26.36195886824082]
  Emotion-Qwen is a unified multimodal framework designed to simultaneously enable robust emotion understanding and preserve general reasoning capabilities.<n>We develop the Video Emotion Reasoning dataset, a large-scale bilingual resource containing over 40K video clips annotated with detailed context-aware emotional descriptions.
  arXiv  Detail & Related papers  (2025-05-10T16:15:26Z)
• 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)
• EmoLLM: Multimodal Emotional Understanding Meets Large Language Models [61.179731667080326]
  Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks. But their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. EmoLLM is a novel model for multimodal emotional understanding, incorporating with two core techniques.
  arXiv  Detail & Related papers  (2024-06-24T08:33:02Z)
• EmoBench: Evaluating the Emotional Intelligence of Large Language Models [73.60839120040887]
  EmoBench is a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine Emotional Intelligence (EI) EmoBench includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing Large Language Models and the average human, highlighting a promising direction for future research.
  arXiv  Detail & Related papers  (2024-02-19T11:48:09Z)
• Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought [50.13429055093534]
  Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks. We propose the Emotional Chain-of-Thought (ECoT) to enhance the performance of LLMs on various emotional generation tasks.
  arXiv  Detail & Related papers  (2024-01-12T16:42:10Z)

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.