From the 2 of 6 linked papers with an AI index.
6 papers
Generation or Judgement? A Paradigm Perspective on LLM-Based Emotion-Cause Pair Extraction in Conversation
Weijie Feng, Hongchuang Wang, Binbin Liu +1
The paper investigates how framing emotion‑cause pair extraction in conversations as either a generation or a pair‑level judgement task impacts large language model performance, fi…
AtmosERC: Modeling Dialogue-Level Affective Atmosphere for Emotion Recognition in Conversation
Weijie Feng, Tongwei Zhang, Binbin Liu +1
The paper introduces AtmosERC, a graph-based framework that captures a dialogue-level affective atmosphere to improve emotion recognition in conversations, providing both lightweig…
Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment
Tianxiang Ma, Weijie Feng, Xinyu Wang +1
Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue. Most e…
Empowering Iterative Graph Alignment Using Heat Diffusion
Boyan Wang, Weijie Feng, Jinyang Huang +2
Unsupervised plain graph alignment (UPGA) aims to align corresponding nodes across two graphs without any auxiliary information. Existing UPGA methods rely on structural consistenc…
AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition
Xuxiong Liu, Tengteng Dong, Fei Wang +2
Micro-expressions are typically regarded as unconscious manifestations of a person's genuine emotions. However, their short duration and subtle signals pose significant challenges…
Compound Expression Recognition via Multi Model Ensemble for the ABAW7 Challenge
Xuxiong Liu, Kang Shen, Jun Yao +6
Compound Expression Recognition (CER) is vital for effective interpersonal interactions. Human emotional expressions are inherently complex due to the presence of compound expressi…