1 code implementation • 21 Dec 2023 • Hai Zhang, Chunwei Wu, Guitao Cao, Hailing Wang, Wenming Cao
Editing real images authentically while also achieving cross-domain editing remains a challenge.
no code implementations • 20 Jan 2023 • Chunwei Wu, Guitao Cao, Yan Li, Xidong Xi, Wenming Cao, Hong Wang
Inspired by this insight, we present Chaos to Order (CtO), a novel approach for SFDA that strives to constrain semantic credibility and propagate label information among target subpopulations.
no code implementations • 29 Sep 2021 • Wenming Cao, Qifan Liu, Guang Liu, Zhihai He
We construct a prime-dual network structure for few-shot learning which establishes a commutative relationship between the support set and the query set, as well as a new self- supervision constraint for highly effective few-shot learning.
no code implementations • 29 Sep 2021 • Wenming Cao, Zhineng Zhao, Qifan Liu, Zhihai He
Few-shot learning (FSL) aims to characterize the inherent visual relationship between support and query samples which can be well generalized to unseen classes so that we can accurately infer the labels of query samples from very few support samples.
no code implementations • 24 Jul 2021 • Wenming Cao, Philip L. H. Yu, Gilbert C. S. Lui, Keith W. H. Chiu, Ho-Ming Cheng, Yanwen Fang, Man-Fung Yuen, Wai-Kay Seto
In this work, we propose a new segmentation network by integrating DenseUNet and bidirectional LSTM together with attention mechanism, termed as DA-BDense-UNet.
no code implementations • CVPR 2021 • Yang Li, Shichao Kan, Jianhe Yuan, Wenming Cao, Zhihai He
It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures.
1 code implementation • 22 Apr 2019 • Yang Hu, Guihua Wen, Mingnan Luo, Dan Dai, Wenming Cao, Zhiwen Yu, Wendy Hall
To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement.
no code implementations • 4 Sep 2018 • Dan Dai, Zhiwen Yu, Yang Hu, Wenming Cao, Mingnan Luo
It is self-evident that the significance of metabolize neuronal network(MetaNet) in model construction.