no code implementations • ECCV 2020 • Arman Karimian, Ziqi Yang, Roberto Tron
In the last few years, there has been an increasing trend to consider Structure from Motion (SfM, in computer vision) and Simultaneous Localization and Mapping (SLAM, in robotics) problems from the point of view of pose averaging (also known as global SfM, in computer vision) or Pose Graph Optimization (PGO, in robotics), where the motion of the camera is reconstructed by considering only relative rigid body transformations instead of including also 3-D points (as done in a full Bundle Adjustment).
1 code implementation • 20 Dec 2023 • Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, Yang Yang
Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.
no code implementations • 17 Sep 2023 • Ziqi Yang, Xuhai Xu, Bingsheng Yao, Shao Zhang, Ethan Rogers, Stephen Intille, Nawar Shara, Guodong Gordon Gao, Dakuo Wang
(2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system.
no code implementations • 24 Aug 2023 • Ziqi Yang, Zhongyu Li, Chen Liu, Xiangde Luo, Xingguang Wang, Dou Xu, CHAOQUN LI, Xiaoying Qin, Meng Yang, Long Jin
To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification.
no code implementations • 21 Jun 2023 • Zheng Wang, Xiaoliang Fan, Zhaopeng Peng, Xueheng Li, Ziqi Yang, Mingkuan Feng, Zhicheng Yang, Xiao Liu, Cheng Wang
Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios.
no code implementations • 1 Dec 2022 • Ziqi Yang, Lijin Wang, Da Yang, Jie Wan, Ziming Zhao, Ee-Chien Chang, Fan Zhang, Kui Ren
Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks.
no code implementations • 8 May 2020 • Ziqi Yang, Bin Shao, Bohan Xuan, Ee-Chien Chang, Fan Zhang
Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier.
no code implementations • 7 Feb 2020 • Arman Karimian, Ziqi Yang, Roberto Tron
This paper introduces a novel and distributed method for detecting inter-map loop closure outliers in simultaneous localization and mapping (SLAM).
no code implementations • 14 Jun 2019 • Ziqi Yang, Hung Dang, Ee-Chien Chang
In this paper, we show that distillation, a widely used transformation technique, is a quite effective attack to remove watermark embedded by existing algorithms.
1 code implementation • 22 Feb 2019 • Ziqi Yang, Ee-Chien Chang, Zhenkai Liang
In this work, we investigate the model inversion problem in the adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values.