1 code implementation • 22 Dec 2023 • Junwei He, Qianqian Xu, Yangbangyan Jiang, Zitai Wang, Qingming Huang
We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns.
1 code implementation • CVPR 2022 • Yunrui Zhao, Qianqian Xu, Yangbangyan Jiang, Peisong Wen, Qingming Huang
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones.
no code implementations • 26 Sep 2022 • Yangbangyan Jiang, Xiaodan Li, Yuefeng Chen, Yuan He, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL).
1 code implementation • CVPR 2021 • Peisong Wen, Qianqian Xu, Yangbangyan Jiang, Zhiyong Yang, Yuan He, Qingming Huang
Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered.
1 code implementation • NeurIPS 2019 • Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang
Instead of transforming all the samples into a joint modality-independent space, our framework learns the mappings across individual modal spaces by virtue of cycle-consistency.
1 code implementation • NeurIPS 2019 • Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang
Different from most of the previous work, pursuing the Block-Diagonal structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed.
no code implementations • CVPR 2019 • Qianqian Xu, Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang, Yuan YAO
The problem of estimating subjective visual properties (SVP) of images (e. g., Shoes A is more comfortable than B) is gaining rising attention.