activity
20192022
most citedTGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning

24 citations · 90 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CV202224 cited

TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning

Linhai Zhuo, Yuqian Fu, Jingjing Chen +2

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target d…

cs.CV202223 cited

ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

Yuqian Fu, Yu Xie, Yanwei Fu +2

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core cha…

math.AP2022

Eternal solutions in exponential self-similar form for a quasilinear reaction-diffusion equation with critical singular potential

Razvan Gabriel Iagar, Marta Latorre, Ariel Sánchez

We prove existence and uniqueness of self-similar solutions with exponential form to the following quasilinear reaction-diffusion…

cs.CV2022

Cross-lingual Adaptation for Recipe Retrieval with Mixup

Bin Zhu, Chong-Wah Ngo, Jingjing Chen +1

Cross-modal recipe retrieval has attracted research attention in recent years, thanks to the availability of large-scale paired data for training. Nevertheless, obtaining adequate…

cs.CV20229 cited

ObjectFormer for Image Manipulation Detection and Localization

Junke Wang, Zuxuan Wu, Jingjing Chen +4

Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this…

cs.CV2022

Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning

Yuqian Fu, Yu Xie, Yanwei Fu +2

Previous few-shot learning (FSL) works mostly are limited to natural images of general concepts and categories. These works assume very high visual similarity between the source an…