45 citations · 105 across the 10 of their papers we have counts for
10 papers
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
Dun Zeng, Zenglin Xu, Yu Pan +3
Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset…
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Zhipeng Zhang, Piao Tong, Yingwei Ma +3
Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences…
DETA: Denoised Task Adaptation for Few-Shot Learning
Ji Zhang, Lianli Gao, Xu Luo +2
Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled suppo…
A Closer Look at Few-shot Classification Again
Xu Luo, Hao Wu, Ji Zhang +3
Few-shot classification consists of a training phase where a model is learned on a relatively large dataset and an adaptation phase where the learned model is adapted to previously…
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
Jing Xu, Xu Luo, Xinglin Pan +3
Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning metho…
Channel Importance Matters in Few-Shot Image Classification
Xu Luo, Jing Xu, Zenglin Xu
Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this ta…
Exploring Category-correlated Feature for Few-shot Image Classification
Jing Xu, Xinglin Pan, Xu Luo +2
Few-shot classification aims to adapt classifiers to novel classes with a few training samples. However, the insufficiency of training data may cause a biased estimation of feature…
Adaptive Multi-layer Contrastive Graph Neural Networks
Shuhao Shi, Pengfei Xie, Xu Luo +4
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of…
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
Xu Luo, Yuxuan Chen, Liangjian Wen +2
The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classificati…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…