1 citations · 4 across the 18 of their papers we have counts for
21 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…
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling
Shuo Xu, Haokai Ma, Yunshan Ma +4
Product bundling aims to organize a set of thematically related items into a combined bundle for shipment facilitation and item promotion. To increase the exposure of fresh or over…
Negative Sampling in Recommendation: A Survey and Future Directions
Haokai Ma, Ruobing Xie, Lei Meng +5
Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``in…
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…
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
Zhuang Qi, Lei Meng, Zitan Chen +3
Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different cli…
Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation
Haokai Ma, Zhuang Qi, Xinxin Dong +3
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically in…
Mlinear: Rethink the Linear Model for Time-series Forecasting
Wei Li, Xiangxu Meng, Chuhao Chen +1
Recently, significant advancements have been made in time-series forecasting research, with an increasing focus on analyzing the nature of time-series data, e.g, channel-independen…
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Yuqing Wang, Xiangxian Li, Zhuang Qi +4
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features. However, conventional meth…