2 citations · 3 across the 10 of their papers we have counts for
10 papers
MOTOR-Bench: A Real-world Dataset and Multi-agent Framework for Zero-shot Human Mental State Understanding
Xiaoyu Yuan, Niklas Heikkala, Tiina Törmänen +3
Understanding human mental states from natural behavior is crucial for intelligent systems in the real world. However, most current research focuses on predicting isolated mental s…
AffectGPT-RL: Revealing Roles of Reinforcement Learning in Open-Vocabulary Emotion Recognition
Zheng Lian, Fan Zhang, Lan Chen +8
Open-Vocabulary Multimodal Emotion Recognition (OV-MER) aims to predict emotions without being constrained by predefined label spaces, thereby enabling fine-grained emotion underst…
RobotEQ: Towards Social Proactive Intelligence in Embodied Agents
Kuofei Fang, Zheng Lian, Xinyi Che +13
Embodied agents represent a prominent research focus across both academia and industry. The prevailing paradigm has gradually shifted from reactive assistance, which requires expli…
iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis
Sofoklis Kakouros, Fang Kang, Haoyu Chen
This work presents iMiGUE-Speech, an extension of the iMiGUE dataset that provides a spontaneous affective corpus for studying emotional and affective states. The new release focus…
SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?
Yueyi Yang, Haotian Liu, Fang Kang +4
We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating their ability to engage in natur…
AffectGPT-R1: Leveraging Reinforcement Learning for Open-Vocabulary Multimodal Emotion Recognition
Zheng Lian, Fan Zhang, Yazhou Zhang +5
Open-Vocabulary Multimodal Emotion Recognition (OV-MER) aims to predict emotions without being constrained by label spaces, enabling fine-grained emotion understanding. Unlike trad…
Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions
Licai Sun, Xingxun Jiang, Haoyu Chen +7
Current facial emotion recognition systems are predominately trained to predict a fixed set of predefined categories or abstract dimensional values. This constrained form of superv…
EmoPrefer: Can Large Language Models Understand Human Emotion Preferences?
Zheng Lian, Licai Sun, Lan Chen +8
Descriptive Multimodal Emotion Recognition (DMER) has garnered increasing research attention. Unlike traditional discriminative paradigms that rely on predefined emotion taxonomies…
AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models
Zheng Lian, Haoyu Chen, Lan Chen +9
The emergence of multimodal large language models (MLLMs) advances multimodal emotion recognition (MER) to the next level, from naive discriminative tasks to complex emotion unders…
OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition
Zheng Lian, Haiyang Sun, Licai Sun +13
Multimodal Emotion Recognition (MER) is a critical research area that seeks to decode human emotions from diverse data modalities. However, existing machine learning methods predom…