146 citations · 204 across the 6 of their papers we have counts for
6 papers
Cooperative Bi-path Metric for Few-shot Learning
Zeyuan Wang, Yifan Zhao, Jia Li +1
Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most e…
Single Image Dehazing Using Ranking Convolutional Neural Network
Yafei Song, Jia Li, Xiaogang Wang +1
Single image dehazing, which aims to recover the clear image solely from an input hazy or foggy image, is a challenging ill-posed problem. Analysing existing approaches, the common…
Zooming into Face Forensics: A Pixel-level Analysis
Jia Li, Tong Shen, Wei Zhang +3
The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society. It is urgent to have…
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
Jia Li, Wen Su, Zengfu Wang
We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an…
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
Limeng Qiao, Yemin Shi, Jia Li +3
Few-shot learning, which aims at extracting new concepts rapidly from extremely few examples of novel classes, has been featured into the meta-learning paradigm recently. Yet, the…
AutoScaler: Scale-Attention Networks for Visual Correspondence
Shenlong Wang, Linjie Luo, Ning Zhang +1
Finding visual correspondence between local features is key to many computer vision problems. While defining features with larger contextual scales usually implies greater discrimi…