3 citations · 7 across the 7 of their papers we have counts for
7 papers
Hardness-Aware Dynamic Curriculum Learning for Robust Multimodal Emotion Recognition with Missing Modalities
Rui Liu, Haolin Zuo, Zheng Lian +2
Missing modalities have recently emerged as a critical research direction in multimodal emotion recognition (MER). Conventional approaches typically address this issue through miss…
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…
Leveraging Retrieval Augment Approach for Multimodal Emotion Recognition Under Missing Modalities
Qi Fan, Hongyu Yuan, Haolin Zuo +2
Multimodal emotion recognition utilizes complete multimodal information and robust multimodal joint representation to gain high performance. However, the ideal condition of full mo…
Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset
Rui Liu, Haolin Zuo, Zheng Lian +3
Emotion and Intent Joint Understanding in Multimodal Conversation (MC-EIU) aims to decode the semantic information manifested in a multimodal conversational history, while inferrin…
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios
Qi Fan, Haolin Zuo, Rui Liu +2
Multimodal emotion recognition (MER) in practical scenarios is significantly challenged by the presence of missing or incomplete data across different modalities. To overcome these…
Explicit Intensity Control for Accented Text-to-speech
Rui Liu, Haolin Zuo, De Hu +2
Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent in the proces…
Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities
Haolin Zuo, Rui Liu, Jinming Zhao +2
Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always pr…