no code implementations • 23 Feb 2023 • Minghao Guo, Yan Gao, Zheng Pan
Converting a parametric curve into the implicit form, which is called implicitization, has always been a popular but challenging problem in geometric modeling and related applications.
1 code implementation • sci 2022 • Junhua Zhang, Minghao Guo, Pengzhi Chu, Yang Liu, Jun Chen, Huanxi Liu
Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries.
1 code implementation • ICLR 2022 • Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik
This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data.
no code implementations • 5 May 2021 • Minghao Guo, Wan Shou, Liane Makatura, Timothy Erps, Michael Foshey, Wojciech Matusik
Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers.
1 code implementation • CVPR 2021 • Zhaoyang Lyu, Minghao Guo, Tong Wu, Guodong Xu, Kehuan Zhang, Dahua Lin
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks.
1 code implementation • 28 Sep 2020 • Rui Xu, Minghao Guo, Jiaqi Wang, Xiaoxiao Li, Bolei Zhou, Chen Change Loy
By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.
1 code implementation • CVPR 2020 • Minghao Guo, Yuzhe Yang, Rui Xu, Ziwei Liu, Dahua Lin
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks.
1 code implementation • ICCV 2019 • Chen Lin, Minghao Guo, Chuming Li, Yuan Xin, Wei Wu, Dahua Lin, Wanli Ouyang, Junjie Yan
Data augmentation is critical to the success of modern deep learning techniques.
1 code implementation • ICCV 2019 • Chuming Li, Yuan Xin, Chen Lin, Minghao Guo, Wei Wu, Wanli Ouyang, Junjie Yan
The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation.
no code implementations • 14 Apr 2019 • Yung-Hsiang Lu, George K. Thiruvathukal, Ahmed S. Kaseb, Kent Gauen, Damini Rijhwani, Ryan Dailey, Deeptanshu Malik, Yutong Huang, Sarah Aghajanzadeh, Minghao Guo
This paper describes the real-time data available from worldwide network cameras and potential applications.
1 code implementation • CVPR 2019 • Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, Junjie Yan
Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt).
no code implementations • ECCV 2018 • Minghao Guo, Jiwen Lu, Jie zhou
In this paper, we propose a dual-agent deep reinforcement learning (DADRL) method for deformable face tracking, which generates bounding boxes and detects facial landmarks interactively from face videos.