105 citations · 433 across the 42 of their papers we have counts for
11 papers · 1 filter
Generative Kernel Continual learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao +1
Kernel continual learning by \citet{derakhshani2021kernel} has recently emerged as a strong continual learner due to its non-parametric ability to tackle task interference and cata…
Multi-Task Neural Processes
Jiayi Shen, Xiantong Zhen, Marcel Worring +1
Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can enco…
Hierarchical Variational Memory for Few-shot Learning Across Domains
Yingjun Du, Xiantong Zhen, Ling Shao +1
Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generaliz…
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation
Hongjun Chen, Jinbao Wang, Hong Cai Chen +4
Annotation burden has become one of the biggest barriers to semantic segmentation. Approaches based on click-level annotations have therefore attracted increasing attention due to…
Variational Topic Inference for Chest X-Ray Report Generation
Ivona Najdenkoska, Xiantong Zhen, Marcel Worring +1
Automating report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successf…
Kernel Continual Learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao +1
This paper introduces kernel continual learning, a simple but effective variant of continual learning that leverages the non-parametric nature of kernel methods to tackle catastrop…
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
Yingjun Du, Nithin Holla, Xiantong Zhen +2
A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. Thi…
MetaKernel: Learning Variational Random Features with Limited Labels
Yingjun Du, Haoliang Sun, Xiantong Zhen +4
Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-sho…
Attentional Prototype Inference for Few-Shot Segmentation
Haoliang Sun, Xiankai Lu, Haochen Wang +4
This paper aims to address few-shot segmentation. While existing prototype-based methods have achieved considerable success, they suffer from uncertainty and ambiguity caused by li…
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Zehao Xiao, Jiayi Shen, Xiantong Zhen +2
Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with…
Variational Knowledge Distillation for Disease Classification in Chest X-Rays
Tom van Sonsbeek, Xiantong Zhen, Marcel Worring +1
Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electr…