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Junyi Wang

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4 papers

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NeurIPS Conference 2025 Conference Paper

3D Gaussian Splatting based Scene-independent Relocalization with Unidirectional and Bidirectional Feature Fusion

Junyi Wang
Yuze Wang
Wantong Duan
Meng Wang
Yue Qi

Visual localization is a critical component across various domains. The recent emergence of novel scene representations, such as 3D Gaussian Splatting (3D GS), introduces new opportunities for advancing localization pipelines. In this paper, we propose a novel 3D GS-based framework for RGB based, scene-independent camera relocalization, with three main contributions. First, we design a two-stage pipeline with fully exploiting 3D GS. The pipeline consists of an initial stage, which utilizes 2D-3D correspondences between image pixels and 3D Gaussians, followed by pose refinement using the rendered image by 3D GS. Second, we introduce a 3D GS based Relocalization Network, termed GS-RelocNet, to establish correspondences for initial camera pose estimation. Additionally, we present a refinement network that further optimizes the camera pose. Third, we propose a unidirectional 2D-3D feature fusion module and a bidirectional image feature fusion module, integrated into GS-RelocNet and the refinement network, respectively, to enhance feature sharing across the two stages. Experimental results on public 7 Scenes, Cambridge Landmarks, TUM RGB-D and Bonn demonstrate state-of-the-art performance. Furthermore, the beneficial effects of the two feature fusion modules and pose refinement are also highlighted. In summary, we believe that the proposed framework can be a novel universal localization pipeline for further research.

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YNIMG Journal 2025 Journal Article

Deep diffusion MRI template (DDTemplate): A novel deep learning groupwise diffusion MRI registration method for brain template creation

Junyi Wang
Xi Zhu
Wei Zhang
Mubai Du
William M. Wells
Lauren J O’Donnell
Fan Zhang

Diffusion MRI (dMRI) is an advanced imaging technique that enables in-vivo tracking of white matter fiber tracts and estimates the underlying cellular microstructure of brain tissues. Groupwise registration of dMRI data from multiple individuals is an important task for brain template creation and investigation of inter-subject brain variability. However, groupwise registration is a challenging task due to the uniqueness of dMRI data that include multi-dimensional, orientation-dependent signals that describe not only the strength but also the orientation of water diffusion in brain tissues. Deep learning approaches have shown successful performance in standard subject-to-subject dMRI registration. However, no deep learning methods have yet been proposed for groupwise dMRI registration. . In this work, we propose Deep Diffusion MRI Template (DDTemplate), which is a novel deep-learning-based method building upon the popular VoxelMorph framework to take into account dMRI fiber tract information. DDTemplate enables joint usage of whole-brain tissue microstructure and tract-specific fiber orientation information to ensure alignment of white matter fiber tracts and whole brain anatomical structures. We propose a novel deep learning framework that simultaneously trains a groupwise dMRI registration network and generates a population brain template. During inference, the trained model can be applied to register unseen subjects to the learned template. We compare DDTemplate with several state-of-the-art registration methods and demonstrate superior performance on dMRI data from multiple cohorts (adolescents, young adults, and elderly adults) acquired from different scanners. Furthermore, as a testbed task, we perform a between-population analysis to investigate sex differences in the brain, using the popular Tract-Based Spatial Statistics (TBSS) method that relies on groupwise dMRI registration. We find that using DDTemplate can increase the sensitivity in population difference detection, showing the potential of our method's utility in real neuroscientific applications.

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ECAI Conference 2023 Conference Paper

BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization

Junyi Wang
Yuanyang Zhu
Zhi Wang 0001
Yan Zheng 0002
Jianye Hao
Chunlin Chen 0001

Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters). In the paper, we propose a general meta ERL framework via bilevel optimization (BiERL) to jointly update hyperparameters in parallel to training the ERL model within a single agent, which relieves the need for prior domain knowledge or costly optimization procedure before model deployment. We design an elegant meta-level architecture that embeds the inner-level’s evolving experience into an informative population representation and introduce a simple and feasible evaluation of the meta-level fitness function to facilitate learning efficiency. We perform extensive experiments in MuJoCo and Box2D tasks to verify that as a general framework, BiERL outperforms various baselines and consistently improves the learning performance for a diversity of ERL algorithms.

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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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