100 citations · 160 across the 12 of their papers we have counts for
12 papers
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning
Yingjun Du, Jiayi Shen, Xiantong Zhen +1
Few-shot meta-learning presents a challenge for gradient descent optimization due to the limited number of training samples per task. To address this issue, we propose an episodic…
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation
Shuo Chen, Yingjun Du, Pascal Mettes +1
This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of subject, p…
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
Yingjun Du, Zehao Xiao, Shengcai Liao +1
Prototype-based meta-learning has emerged as a powerful technique for addressing few-shot learning challenges. However, estimating a deterministic prototype using a simple average…
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
Wenfang Sun, Yingjun Du, Xiantong Zhen +3
Meta-learning algorithms are able to learn a new task using previously learned knowledge, but they often require a large number of meta-training tasks which may not be readily avai…
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail
Yingjun Du, Jiayi Shen, Xiantong Zhen +1
Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the lon…
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…