13 citations · 22 across the 2 of their papers we have counts for
3 papers
cs.CV2020★ 9 cited
Filter Grafting for Deep Neural Networks
Fanxu Meng, Hao Cheng, Ke Li +4
This paper proposes a new learning paradigm called filter grafting, which aims to improve the representation capability of Deep Neural Networks (DNNs). The motivation is that DNNs…
cs.CV2019★ 13 cited
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification
Fengxiang Yang, Ke Li, Zhun Zhong +7
Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achi…
cs.CV2019
Semi-Supervised Adversarial Monocular Depth Estimation
Rongrong Ji, Ke Li, Yan Wang +6
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available. To achieve a high regression accuracy, t…