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Found 8 papers, 6 papers with code
Emotion-Qwen: Training Hybrid Experts for Unified Emotion and General Vision-Language Understanding
While Large Multimodal Models (LMMs) have demonstrated significant progress in general vision-language (VL) tasks, their performance in emotion-specific scenarios remains limited.
Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models
Most existing emotion analysis emphasizes which emotion arises (e. g., happy, sad, angry) but neglects the deeper why.
Ranked #1 on
Emotion Interpretation
on EIBench (complex)
HA-VLN: A Benchmark for Human-Aware Navigation in Discrete-Continuous Environments with Dynamic Multi-Human Interactions, Real-World Validation, and an Open Leaderboard
Vision-and-Language Navigation (VLN) systems often focus on either discrete (panoramic) or continuous (free-motion) paradigms alone, overlooking the complexities of human-populated, dynamic environments.
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model
To address this, we introduce a new multimodal multi-turn conversational stance detection dataset (called MmMtCSD).
SZTU-CMU at MER2024: Improving Emotion-LLaMA with Conv-Attention for Multimodal Emotion Recognition
This paper presents our winning approach for the MER-NOISE and MER-OV tracks of the MER2024 Challenge on multimodal emotion recognition.
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
Accurate emotion perception is crucial for various applications, including human-computer interaction, education, and counseling.
Dataset Growth
To tackle this challenge, we propose InfoGrowth, an efficient online algorithm for data cleaning and selection, resulting in a growing dataset that keeps up to date with awareness of cleanliness and diversity.
MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models
This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations.
