activity
20212024
most citedMultimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

167 citations · 186 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20241 cited

Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis

Sijie Mai, Yu Zhao, Ying Zeng +2

Multimodal sentiment analysis aims to effectively integrate information from various sources to infer sentiment, where in many cases there are no annotations for unimodal labels. T…

cs.LG20223 cited

Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction

Jianfeng Wu, Sijie Mai, Haifeng Hu

Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relat…

cs.LG2022167 cited

Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

Sijie Mai, Ying Zeng, Haifeng Hu

Learning effective joint embedding for cross-modal data has always been a focus in the field of multimodal machine learning. We argue that during multimodal fusion, the generated m…

cs.AI202113 cited

Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

Sijie Mai, Ying Zeng, Shuangjia Zheng +1

The wide application of smart devices enables the availability of multimodal data, which can be utilized in many tasks. In the field of multimodal sentiment analysis (MSA), most pr…

cs.LG20212 cited

Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning

Shuangjia Zheng, Sijie Mai, Ya Sun +2

Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entit…