277 citations · 1.7k across the 70 of their papers we have counts for
6 papers · 1 filter
Learning to Compare: Relation Network for Few-Shot Learning
Flood Sung, Yongxin Yang, Li Zhang +3
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. O…
Learning to Generalize: Meta-Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generaliz…
Deeper, Broader and Artier Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generaliza…
Weakly Supervised Image Annotation and Segmentation with Objects and Attributes
Zhiyuan Shi, Yongxin Yang, Timothy M. Hospedales +1
We propose to model complex visual scenes using a non-parametric Bayesian model learned from weakly labelled images abundant on media sharing sites such as Flickr. Given weak image…
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
Flood Sung, Li Zhang, Tao Xiang +2
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can…
Actor-Critic Sequence Training for Image Captioning
Li Zhang, Flood Sung, Feng Liu +4
Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users…