11 papers
Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
Zhuang Qi, Pan Yu, Lei Meng +4
Federated Prompt Learning (FPL) enables communication-efficient adaptation by tuning lightweight prompts on top of frozen pre-trained models. Existing FPL methods typically rely on…
Large model retrieval enhancement framework for construction site risk identification
Jiawei Li, Chengye Yang, Yaochen Zhang +3
This study addresses construction site hazard identification by proposing a retrieval-augmented framework that enhances large language models (LLMs) without requiring fine-tuning.…
ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification
Kexuan Shi, Zhuang Qi, Jingjing Zhu +4
Open-set few-shot image classification aims to train models using a small amount of labeled data, enabling them to achieve good generalization when confronted with unknown environm…
Class-wise Balancing Data Replay for Federated Class-Incremental Learning
Zhuang Qi, Ying-Peng Tang, Lei Meng +3
Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay ha…
Semantic-Space-Intervened Diffusive Alignment for Visual Classification
Zixuan Li, Lei Meng, Guoqing Chao +5
Cross-modal alignment is an effective approach to improving visual classification. Existing studies typically enforce a one-step mapping that uses deep neural networks to project t…
Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification
Xiaoshuo Yan, Zhaochuan Li, Lei Meng +4
Causal inference has emerged as a promising approach to mitigate long-tail classification by handling the biases introduced by class imbalance. However, along with the change of ad…