arXiv AI
Jun 12
Emotional regulation improves deep learning-based image classification
arXiv:2606. 13081v1 Announce Type: cross Abstract: Emotion significantly influences cognition, enhancing memory and learning under certain conditions.
Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning
arXiv:2607. 10678v1 Announce Type: new Abstract: Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states.
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arXiv AI
Aug 12
Rationale-Guided Learning for Multimodal Emotion Recognition
arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
Hugging Face Trending Papers
Jul 23
MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement
Affective Image Content Analysis (AICA) aims to recognize and understand emotions elicited by visual content, representing an indispensable step toward Artificial General Intelligence (AGI). However, despite the rapid progress of Multimodal Large Language Models (MLLMs), systematic evaluation of their visual emotional intelligence remains largely absent from recent model releases.
arXiv AI
Jun 18
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
arXiv:2510. 09905v2 Announce Type: replace Abstract: When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive?
arXiv AI
Jul 9
EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models
arXiv:2602. 23802v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture the complexity and subjectivity of human emotions.
arXiv AI
Jul 14
EmoStyle: Affective Conditioning of Style-Specialist Experts for Emotional Image Generation
arXiv:2607. 10165v1 Announce Type: cross Abstract: Emotion-aware artistic image generation requires an image to match the input prompt, follow the specified artistic style, and convey the target emotion.
arXiv AI
Jun 12
EPIG: Emotion-Based Prompting for Personalised Image Generation
arXiv:2606. 13247v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved impressive results in synthesizing high-quality images from natural language prompts.
arXiv AI
Jun 15
EmoMind: Decoding Affective Captions from Human Brain fMRI
arXiv:2605. 16739v2 Announce Type: replace-cross Abstract: Decoding visual experience from brain activity has advanced substantially, but current brain-to-text systems largely recover semantic content while discarding affect.
arXiv Machine Learning
Jun 30
Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions
arXiv:2606. 28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication.
arXiv AI
Jun 3
PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search
arXiv:2606. 03099v1 Announce Type: cross Abstract: Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations.
arXiv Machine Learning
Jun 9
Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework
arXiv:2606. 07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect.
Hugging Face Trending Papers
Aug 11
E$^3$mo-Bench: A Scalable Benchmark for Multimodal Evoked and Expressed Emotion Understanding via Bayesian Pairwise Alignment
Understanding both expressed and evoked emotions is critical for multimodal large language models (MLLMs) to achieve comprehensive affect-aware interactions. However, existing benchmarks typically examine expressed and evoked emotions in isolation or are constrained to coarse-grained and incomplete affective characterizations.