675 citations · 1.7k across the 27 of their papers we have counts for
5 papers · 1 filter
Meta-Transfer Learning through Hard Tasks
Qianru Sun, Yaoyao Liu, Zhaozheng Chen +2
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order t…
Joint Visual Grounding with Language Scene Graphs
Daqing Liu, Hanwang Zhang, Zheng-Jun Zha +2
Visual grounding is a task to ground referring expressions in images, e.g., localize "the white truck in front of the yellow one". To resolve this task fundamentally, the model sho…
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Xinzhe Li, Qianru Sun, Yaoyao Liu +4
Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising…
A Novel BiLevel Paradigm for Image-to-Image Translation
Liqian Ma, Qianru Sun, Bernt Schiele +1
Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong cate…
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
Yaoyao Liu, Bernt Schiele, Qianru Sun
Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predict…