activity
20232026
most citedHiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition

70 citations · 117 across the 19 of their papers we have counts for

collaborators

19 papers

eess.AS2026

MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation

Xuzhi Wang, Xinran Wu, Ziping Zhao +2

Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-constrained mental health setting…

cs.HC2026

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…

cs.HC2026

MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding

Zheng Lian, Xiaojiang Peng, Kele Xu +16

MER2026 marks the fourth edition of the MER series of challenges. The MER series provides valuable data resources to the research community and offers tasks centered on recent rese…

cs.HC2025

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…

cs.HC2025

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…

cs.HC2025

MER 2025: When Affective Computing Meets Large Language Models

Zheng Lian, Rui Liu, Kele Xu +15

MER2025 is the third year of our MER series of challenges, aiming to bring together researchers in the affective computing community to explore emerging trends and future direction…