1 code implementation • ECCV 2020 • Bo Fu, Zhangjie Cao, Mingsheng Long, Jian-Min Wang
The new transferability measure accurately quantifies the inclination of a target example to the open classes.
Ranked #8 on Universal Domain Adaptation on DomainNet
no code implementations • 18 Mar 2024 • Yang Li, Qiuyi Huang, Chong Zhong, Danjuan Yang, Meiyan Li, A. H. Welsh, Aiyi Liu, Bo Fu, Catherien C. Liu, Xingtao Zhou
Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores.
no code implementations • 7 Nov 2023 • Chong Zhong, Yang Li, Danjuan Yang, Meiyan Li, Xingyao Zhou, Bo Fu, Catherine C. Liu, A. H. Welsh
Inspired by the spirit that information extracted from the data by statistical methods can improve the prediction accuracy of deep learning models, we formulate a class of multivariate response regression models with a higher-order tensor biomarker, for the bivariate tasks of regression-classification and regression-regression.
1 code implementation • 13 Dec 2022 • Pei Liu, Luping Ji, Feng Ye, Bo Fu
To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework.
1 code implementation • 12 Jun 2022 • Pei Liu, Bo Fu, Feng Ye, Rui Yang, Bin Xu, Luping Ji
Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4. 6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6. 31M vs. 860. 18K) but less computational costs (2. 51G vs. 4. 94G), compared to a typical existing multi-resolution network.
no code implementations • CVPR 2021 • Bo Fu, Zhangjie Cao, Jianmin Wang, Mingsheng Long
Due to the domain shift, the query selection criteria of prior active learning methods may be ineffective to select the most informative target samples for annotation.
2 code implementations • NeurIPS 2019 • Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jian-Min Wang
Before sufficient training data is available, fine-tuning neural networks pre-trained on large-scale datasets substantially outperforms training from random initialization.
no code implementations • 15 Apr 2019 • Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
no code implementations • 11 Mar 2019 • Patrick O'Driscoll, Jaehoon Lee, Bo Fu
We propose that intelligently combining models from the domains of Artificial Intelligence or Machine Learning with Physical and Expert models will yield a more "trustworthy" model than any one model from a single domain, given a complex and narrow enough problem.
no code implementations • 3 Oct 2018 • Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
no code implementations • 15 Jul 2018 • Bo Fu, Xiao-Yang Zhao, Yong-Gong Ren, Xi-Ming Li, Xiang-Hai Wang
In this paper, an image denoising algorithm is proposed for salt and pepper noise.
no code implementations • COLING 2016 • Xi-Ming Li, Jinjin Chi, Changchun Li, Jihong Ouyang, Bo Fu
Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space.
no code implementations • CVPR 2014 • Chenxi Zhang, Mao Ye, Bo Fu, Ruigang Yang
Each segmented petal is then fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned exemplar petals.
no code implementations • CVPR 2014 • Qing Zhang, Bo Fu, Mao Ye, Ruigang Yang
In this paper we present a novel autonomous pipeline to build a personalized parametric model (pose-driven avatar) using a single depth sensor.