3 citations · 4 across the 8 of their papers we have counts for
9 papers
Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination
Kaixin Xu, NaiJin Liu, Yulin Kang +8
Incomplete multimodal sentiment analysis has garnered significant attention in recent years. Existing approaches typically assume that data is missing at random or are designed spe…
Taming the Implicit: Dual-Channel Risk-Aware Reinforcement Fine-Tuning for Continual Multimodal Post-Training
Yibei Liu, Jiajun Chen, Qianle Zhang +4
Reinforcement fine-tuning (RFT) is widely believed to inherently resist catastrophic forgetting in continual post-training of multimodal large language models. Under pronounced tas…
E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental Learning
Jiajun Chen, Yue Wu, Kai Huang +6
Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are…
TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities
Jiajun Chen, Yangyang Wu, Xiaoye Miao +2
The widespread presence of incomplete modalities in multimodal data poses a significant challenge to achieving accurate rumor detection. Existing multimodal rumor detection methods…
A Robust Incomplete Multimodal Low-Rank Adaptation Approach for Emotion Recognition
Xinkui Zhao, Jinsong Shu, Yangyang Wu +6
Multimodal Emotion Recognition (MER) often encounters incomplete multimodality in practical applications due to sensor failures or privacy protection requirements. While existing m…
ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning
Wei Ni, Kaihang Zhang, Xiaoye Miao +4
Error detection (ED) in tabular data is crucial yet challenging due to diverse error types and the need for contextual understanding. Traditional ED methods often rely heavily on m…
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning
Xiaoye Miao, Bin Li, Yanzhang +2
Privacy concerns arise as sensitive data proliferate. Despite decentralized federated learning (DFL) aggregating gradients from neighbors to avoid direct data transmission, it stil…
Automatic Data Repair: Are We Ready to Deploy?
Wei Ni, Xiaoye Miao, Xiangyu Zhao +2
Data quality is paramount in today's data-driven world, especially in the era of generative AI. Dirty data with errors and inconsistencies usually leads to flawed insights, unrelia…
Differentiable and Scalable Generative Adversarial Models for Data Imputation
Yangyang Wu, Jun Wang, Xiaoye Miao +2
Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally i…