1 citations · 4 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
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…