EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis

• URL: http://arxiv.org/abs/2511.12554v1
• Date: Sun, 16 Nov 2025 11:16:50 GMT
• Title: EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis
• Authors: Yijie Guo, Dexiang Hong, Weidong Chen, Zihan She, Cheng Ye, Xiaojun Chang, Zhendong Mao,
• Abstract summary: EmoVerse is a large-scale open-source dataset that enables interpretable visual emotion analysis.<n>With over 219k images, the dataset further includes dual annotations in Categorical Emotion States (CES) and Dimensional Emotion Space (DES)
• Score: 61.87711517626139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Emotion Analysis (VEA) aims to bridge the affective gap between visual content and human emotional responses. Despite its promise, progress in this field remains limited by the lack of open-source and interpretable datasets. Most existing studies assign a single discrete emotion label to an entire image, offering limited insight into how visual elements contribute to emotion. In this work, we introduce EmoVerse, a large-scale open-source dataset that enables interpretable visual emotion analysis through multi-layered, knowledge-graph-inspired annotations. By decomposing emotions into Background-Attribute-Subject (B-A-S) triplets and grounding each element to visual regions, EmoVerse provides word-level and subject-level emotional reasoning. With over 219k images, the dataset further includes dual annotations in Categorical Emotion States (CES) and Dimensional Emotion Space (DES), facilitating unified discrete and continuous emotion representation. A novel multi-stage pipeline ensures high annotation reliability with minimal human effort. Finally, we introduce an interpretable model that maps visual cues into DES representations and provides detailed attribution explanations. Together, the dataset, pipeline, and model form a comprehensive foundation for advancing explainable high-level emotion understanding.

Related papers

• EmoLat: Text-driven Image Sentiment Transfer via Emotion Latent Space [8.453871826832478]
  We propose EmoLat, a novel emotion latent space that enables fine-grained, text-driven image sentiment transfer.<n>Within EmoLat, an emotion semantic graph is constructed to capture the relational structure among emotions, objects, and visual attributes.<n>Building upon EmoLat, a cross-modal sentiment transfer framework is proposed to manipulate image sentiment via joint embedding of text and EmoLat features.
  arXiv  Detail & Related papers  (2026-01-17T15:07:36Z)
• 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)
• Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content [56.62027582702816]
  Multimodal Sentiment Analysis seeks to unravel human emotions by amalgamating text, audio, and visual data.<n>Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge.<n>We introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions.
  arXiv  Detail & Related papers  (2024-12-12T11:30:41Z)
• UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception [8.54013419046987]
  We introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework for visual emotion analysis.<n>By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations.<n>We develop a visual emotional dataset titled Emo8, covering nearly all common emotional scenes.
  arXiv  Detail & Related papers  (2024-09-27T16:12:51Z)
• EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes [53.95428298229396]
  We introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes.
  arXiv  Detail & Related papers  (2023-07-16T06:42:46Z)
• SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
  We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
  arXiv  Detail & Related papers  (2021-10-24T02:41:41Z)
• Affective Image Content Analysis: Two Decades Review and New Perspectives [132.889649256384]
  We will comprehensively review the development of affective image content analysis (AICA) in the recent two decades. We will focus on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
  arXiv  Detail & Related papers  (2021-06-30T15:20:56Z)
• A Circular-Structured Representation for Visual Emotion Distribution Learning [82.89776298753661]
  We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
  arXiv  Detail & Related papers  (2021-06-23T14:53:27Z)

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.