1 code implementation • 29 Dec 2024 • Yan Luo, Muhammad Osama Khan, Congcong Wen, Muhammad Muneeb Afzal, Titus Fidelis Wuermeling, Min Shi, Yu Tian, Yi Fang, Mengyu Wang
To address this critical concern, we present the first comprehensive study on the fairness of medical text-to-image diffusion models.
1 code implementation • 29 Dec 2024 • Yan Luo, Congcong Wen, Min Shi, Hao Huang, Yi Fang, Mengyu Wang
We present a comprehensive theoretical framework analyzing the relationship between data distributions and fairness guarantees in equitable deep learning.
no code implementations • 24 Nov 2024 • Leila Gheisi, Henry Chu, Raju Gottumukkala, Yan Luo, Xingquan Zhu, Mengyu Wang, Min Shi
TransFair aims to transfer a fairness-enhanced disease classification model to the task of progression prediction with fairness preserved.
2 code implementations • 28 Aug 2024 • Min Shi, Fuxiao Liu, Shihao Wang, Shijia Liao, Subhashree Radhakrishnan, De-An Huang, Hongxu Yin, Karan Sapra, Yaser Yacoob, Humphrey Shi, Bryan Catanzaro, Andrew Tao, Jan Kautz, Zhiding Yu, Guilin Liu
We discover that simply concatenating visual tokens from a set of complementary vision encoders is as effective as more complex mixing architectures or strategies.
1 code implementation • 11 Jul 2024 • Yu Tian, Congcong Wen, Min Shi, Muhammad Muneeb Afzal, Hao Huang, Muhammad Osama Khan, Yan Luo, Yi Fang, Mengyu Wang
However, the fairness issue under the setting of domain transfer is almost unexplored, while it is common that clinics rely on different imaging technologies (e. g., different retinal imaging modalities) for patient diagnosis.
1 code implementation • 2 May 2024 • Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, Jose M. Alvarez
We further propose OmniDrive-nuScenes, a new visual question-answering dataset challenging the true 3D situational awareness of a model with comprehensive visual question-answering (VQA) tasks, including scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making and planning.
1 code implementation • CVPR 2024 • Tianqi Liu, Xinyi Ye, Min Shi, Zihao Huang, Zhiyu Pan, Zhan Peng, Zhiguo Cao
We incorporate the above ACA, SVA, and CAF into a coarse-to-fine framework, termed Geometry-aware Reconstruction and Fusion-refined Rendering (GeFu).
1 code implementation • CVPR 2024 • Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang
Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions.
1 code implementation • 4 Mar 2024 • Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Shuai Yang, Tengfei Wang, Liang Pan, Dahua Lin, Ziwei Liu
Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping.
1 code implementation • 3 Nov 2023 • Yu Tian, Min Shi, Yan Luo, Ava Kouhana, Tobias Elze, Mengyu Wang
Existing medical fairness datasets are all for classification tasks, and no fairness datasets are available for medical segmentation, while medical segmentation is an equally important clinical task as classifications, which can provide detailed spatial information on organ abnormalities ready to be assessed by clinicians.
no code implementations • 3 Oct 2023 • Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang
To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes.
1 code implementation • ICCV 2023 • Tianqi Liu, Xinyi Ye, Weiyue Zhao, Zhiyu Pan, Min Shi, Zhiguo Cao
This constraint reduces the 2D search space into the epipolar line in stereo matching.
Ranked #4 on
3D Reconstruction
on DTU
no code implementations • 11 Sep 2023 • Pengfei Yao, Tianlu Mao, Min Shi, Jingkai Sun, Zhaoqi Wang
We introduce expert attention, which adjusts the weights of different depths of network layers, avoiding the model updated slowly due to gradient problem and enabling fast learning of new scenario's knowledge to restore prediction accuracy.
no code implementations • ICCV 2023 • Yan Luo, Min Shi, Yu Tian, Tobias Elze, Mengyu Wang
This is the largest glaucoma detection dataset with 3D OCT imaging data and the first glaucoma progression forecasting dataset that is publicly available.
1 code implementation • 3 Aug 2023 • Weiyun Wang, Min Shi, Qingyun Li, Wenhai Wang, Zhenhang Huang, Linjie Xing, Zhe Chen, Hao Li, Xizhou Zhu, Zhiguo Cao, Yushi Chen, Tong Lu, Jifeng Dai, Yu Qiao
We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world.
2 code implementations • ICCV 2023 • Yiran Wang, Min Shi, Jiaqi Li, Chaoyi Hong, Zihao Huang, Juewen Peng, Zhiguo Cao, Jianming Zhang, Ke Xian, Guosheng Lin
Our work serves as a solid baseline and data foundation for learning-based video depth estimation.
Ranked #19 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
1 code implementation • 15 Jun 2023 • Yan Luo, Yu Tian, Min Shi, Louis R. Pasquale, Lucy Q. Shen, Nazlee Zebardast, Tobias Elze, Mengyu Wang
To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection.
1 code implementation • CVPR 2023 • Min Shi, Zihao Huang, Xianzheng Ma, Xiaowei Hu, Zhiguo Cao
To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals.
Ranked #5 on
2D Pose Estimation
on MP-100
1 code implementation • 10 Nov 2022 • Xiaowei Hu, Min Shi, Weiyun Wang, Sitong Wu, Linjie Xing, Wenhai Wang, Xizhou Zhu, Lewei Lu, Jie zhou, Xiaogang Wang, Yu Qiao, Jifeng Dai
Vision transformers have gained popularity recently, leading to the development of new vision backbones with improved features and consistent performance gains.
1 code implementation • 2 Sep 2022 • Min Shi, Anagha Lokhande, Mojtaba S. Fazli, Vishal Sharma, Yu Tian, Yan Luo, Louis R. Pasquale, Tobias Elze, Michael V. Boland, Nazlee Zebardast, David S. Friedman, Lucy Q. Shen, Mengyu Wang
Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e. g., glaucoma).
1 code implementation • 31 Jul 2022 • Yinpeng Chen, Zhiyu Pan, Min Shi, Hao Lu, Zhiguo Cao, Weicai Zhong
Generative adversarial networks (GANs) have been trained to be professional artists able to create stunning artworks such as face generation and image style transfer.
1 code implementation • 15 Jul 2022 • Yizheng Wu, Min Shi, Shuaiyuan Du, Hao Lu, Zhiguo Cao, Weicai Zhong
The idea of instance kernel is inspired by recent success of dynamic convolutions in 2D/3D instance segmentation.
Ranked #2 on
3D Instance Segmentation
on S3DIS
(mCov metric)
1 code implementation • CVPR 2022 • Min Shi, Hao Lu, Chen Feng, Chengxin Liu, Zhiguo Cao
In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric.
Ranked #4 on
Object Counting
on CARPK
no code implementations • 12 Aug 2021 • Yu Huang, James Li, Min Shi, Hanqi Zhuang, Xingquan Zhu, Laurent Chérubin, James VanZwieten, Yufei Tang
A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.
no code implementations • 11 Aug 2021 • Yu Huang, Yufei Tang, Xingquan Zhu, Min Shi, Ali Muhamed Ali, Hanqi Zhuang, Laurent Cherubin
To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.
no code implementations • 17 May 2021 • Andrey Ignatov, Grigory Malivenko, David Plowman, Samarth Shukla, Radu Timofte, Ziyu Zhang, Yicheng Wang, Zilong Huang, Guozhong Luo, Gang Yu, Bin Fu, Yiran Wang, Xingyi Li, Min Shi, Ke Xian, Zhiguo Cao, Jin-Hua Du, Pei-Lin Wu, Chao Ge, Jiaoyang Yao, Fangwen Tu, Bo Li, Jung Eun Yoo, Kwanggyoon Seo, Jialei Xu, Zhenyu Li, Xianming Liu, Junjun Jiang, Wei-Chi Chen, Shayan Joya, Huanhuan Fan, Zhaobing Kang, Ang Li, Tianpeng Feng, Yang Liu, Chuannan Sheng, Jian Yin, Fausto T. Benavide
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
no code implementations • 12 Apr 2021 • Cong Li, Min Shi, Bo Qu, Xiang Li
In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations.
1 code implementation • 21 Sep 2020 • Min Shi, David A. Wilson, Xingquan Zhu, Yu Huang, Yuan Zhuang, Jianxun Liu, Yufei Tang
In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
no code implementations • 24 Mar 2020 • Min Shi, Jia-Qi Zhang, Shu-Yu Chen, Lin Gao, Yu-Kun Lai, Fang-Lue Zhang
The color transform network takes the target line art images as well as the line art and color images of one or more reference images as input, and generates corresponding target color images.
no code implementations • 26 Dec 2019 • Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu
The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning.
Ranked #32 on
Multi-Label Classification
on MS-COCO
no code implementations • 26 Dec 2019 • Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu
By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task.
no code implementations • 5 Jun 2019 • Olga A Vsevolozhskaya, Min Shi, Fengjiao Hu, Dmitri V Zaykin
Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information.
Genomics Applications