7 papers
Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data
Thu Hang Phung, Duong M. Nguyen, Thanh Trung Huynh +3
This paper introduces a generalized federated prompt-tuning framework for practical scenarios where local datasets are multi-modal and exhibit different distributional patterns of…
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham +3
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared gl…
Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh +2
Multimodal federated learning in real-world settings often encounters incomplete and heterogeneous data across clients. This results in misaligned local feature representations tha…
Expressive and Scalable Quantum Fusion for Multimodal Learning
Tuyen Nguyen, Trong Nghia Hoang, Phi Le Nguyen +2
The aim of this paper is to introduce a quantum fusion mechanism for multimodal learning and to establish its theoretical and empirical potential. The proposed method, called the Q…
Boosting Offline Optimizers with Surrogate Sensitivity
Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong +1
Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in s…
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Thanh Tam Nguyen, Zhao Ren, Trinh Pham +4
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require si…