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Rong Yu

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IJCAI Conference 2025 Conference Paper

VimGeo: Efficient Cross-View Geo-Localization with Vision Mamba Architecture

Jinglin Huang
Maoqiang Wu
Peichun Li
Wen Wu
Rong Yu

Cross-view geo-localization is a crucial task with diverse applications, yet it remains challenging due to the significant variations in viewpoints and visual appearances between images from different perspectives. While recent advancements have been made, existing methods often suffer from high model complexity, excessive resource consumption, and the impact of sample learning difficulty on optimization. To overcome these limitations, we optimize the Vision Mamba (Vim) model, built on a State Space Model (SSM) architecture, by replacing the traditional classification head with Channel Group Pooling (CGP) for efficient feature integration. This optimization reduces model parameters by 1. 5% and computational complexity by 0. 4%. Additionally, we propose a novel Dynamic Weighted Batch-tuple Loss (DWBL) to dynamically adjust the weighting of negative samples, improving model performance. By combining CGP and DWBL, we develop an efficient end-to-end network, VimGeo, which achieves state-of-the-art performance with enhanced computational efficiency. Specifically, VimGeo achieves a Recall@1 of 81. 67% on the CVACT_test dataset, outperforming prior approaches. Extensive experiments on CVUSA, CVACT, and VIGOR datasets validate VimGeo's effectiveness and competitiveness in cross-view geo-localization tasks, achieving the leading results among sequence modeling-based methods. The implementation is available at: https: //github. com/VimGeoTeam/VimGeo.

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AAAI Conference 2020 Conference Paper

Differentially Private and Fair Classification via Calibrated Functional Mechanism

Jiahao Ding
Xinyue Zhang
Xiaohuan Li
Junyi Wang
Rong Yu
Miao Pan

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e. g. , sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classiﬁcation model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefﬁcients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving (, δ)-differential privacy. Based on the relaxed functional mechanism, we can design (, δ)-differentially private and fair classiﬁcation model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.

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