15 papers
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
Zhuang Qi, Ying-Peng Tang, Lei Meng +4
Exemplar replay has become an effective strategy for mitigating catastrophic forgetting in federated continual learning (FCL) by retaining representative samples from past tasks. E…
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…
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization
Zhuang Qi, Sijin Zhou, Lei Meng +3
Attribute bias in federated learning (FL) typically leads local models to optimize inconsistently due to the learning of non-causal associations, resulting degraded performance. Ex…
Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theory
Xiaozheng Qu, Zhaochuan Li, Zhuang Qi +4
The clustering performance of Fuzzy Adaptive Resonance Theory (Fuzzy ART) is highly dependent on the preset vigilance parameter, where deviations in its value can lead to significa…
LLM-Enabled Style and Content Regularization for Personalized Text-to-Image Generation
Anran Yu, Wei Feng, Yaochen Zhang +4
The personalized text-to-image generation has rapidly advanced with the emergence of Stable Diffusion. Existing methods, which typically fine-tune models using embedded identifiers…
Global Intervention and Distillation for Federated Out-of-Distribution Generalization
Zhuang Qi, Runhui Zhang, Lei Meng +3
Attribute skew in federated learning leads local models to focus on learning non-causal associations, guiding them towards inconsistent optimization directions, which inevitably re…
Hierarchically-Structured Open-Vocabulary Indoor Scene Synthesis with Pre-trained Large Language Model
Weilin Sun, Xinran Li, Manyi Li +3
Indoor scene synthesis aims to automatically produce plausible, realistic and diverse 3D indoor scenes, especially given arbitrary user requirements. Recently, the promising genera…
Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment
Yuze Zheng, Zixuan Li, Xiangxian Li +4
Image classification models often demonstrate unstable performance in real-world applications due to variations in image information, driven by differing visual perspectives of sub…
Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity
Zhuang Qi, Lei Meng, Ruohan Zhang +5
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve generalization capability. However, due to inhere…
Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning
Zitan Chen, Zhuang Qi, Xiao Cao +3
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structur…