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Zhuoran Sun

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Earth-Adapter: Bridge the Geospatial Domain Gaps with a Frequency-Guided Mixture of Adapters

Xiaoxing Hu
ZiYang Gong
Yupei Wang
Yuru Jia
Fei Lin
Dexiang Gao
Ke An
Jianhong Han

Vision Foundation Models (VFMs), while powerful, often struggle in Remote Sensing (RS) segmentation tasks when combined with existing Parameter-Efficient Fine-Tuning (PEFT) methods. We observe that this limitation primarily arises from their inability to effectively handle the pervasive artifacts in RS imagery. To address this, we introduce Earth-Adapter, the first PEFT method specifically designed for RS artifact mitigation. Earth-Adapter introduces a novel Frequency-Guided Mixture of Adapters (MoA) approach, structured around a ''divide and conquer" strategy. It first utilizes Discrete Fourier Transformation (DFT) to "divide" features into distinct frequency components, thereby effectively isolating artifact-related information from semantic signals. Subsequently, to ''conquer" these artifact, MoA independently optimizes features within different subspaces and dynamically assigns weights via a router to aggregate the refined representations. This enables adaptive refinement of the VFM’s representation space to mitigate the impact of artifacts. This simple yet highly effective PEFT method demonstrably mitigates artifacts and significantly enhances VFMs performance on RS segmentation tasks. Extensive experiments demonstrate Earth-Adapter's effectiveness on in-domain semantic segmentation (SS), as well as Domain Adaptive (DA) and Domain Generalized (DG) semantic segmentation tasks. Compared with the baseline Rein, Earth-Adapter significantly improves mIoU by 1.2% in SS, 9.0% in DA, and 3.1% in DG benchmarks. Our code and weights will be released soon.

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AAAI Conference 2025 Conference Paper

Conditional Diffusion Models Based Conditional Independence Testing

Yanfeng Yang
Shuai Li
Yingjie Zhang
Zhuoran Sun
Hai Shu
Ziqi Chen
Renming Zhang

Conditional independence (CI) testing is a fundamental task in modern statistics and machine learning. The conditional randomization test (CRT) was recently introduced to test whether two random variables, X and Y, are conditionally independent given a potentially high-dimensional set of random variables, Z. The CRT operates exceptionally well under the assumption that the conditional distribution X|Z is known. However, since this distribution is typically unknown in practice, accurately approximating it becomes crucial. In this paper, we propose using conditional diffusion models (CDMs) to learn the distribution of X|Z. Theoretically and empirically, it is shown that CDMs closely approximate the true conditional distribution. Furthermore, CDMs offer a more accurate approximation of X|Z compared to GANs, potentially leading to a CRT that performs better than those based on GANs. To accommodate complex dependency structures, we utilize a computationally efficient classifier-based conditional mutual information (CMI) estimator as our test statistic. The proposed testing procedure performs effectively without requiring assumptions about specific distribution forms or feature dependencies, and is capable of handling mixed-type conditioning sets that include both continuous and discrete variables. Theoretical analysis shows that our proposed test achieves a valid control of the type I error. A series of experiments on synthetic data demonstrates that our new test effectively controls both type-I and type-II errors, even in high dimensional scenarios.

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IROS Conference 2024 Conference Paper

RTTF: Rapid Tactile Transfer Framework for Contact-Rich Manipulation Tasks

Qiwei Wu 0001
Xuanbin Peng
Jiayu Zhou
Zhuoran Sun
Xiaogang Xiong
Yunjiang Lou

An increasing number of robotic manipulation tasks now use optical tactile sensors to provide tactile feedback, making tactile servo control a crucial aspect of robotic operations. This paper presents a rapid tactile transfer framework (RTTF) that achieves optical-tactile image sim2real transfer and robust tactile servo control using limited paired data. The sim2real aspect of RTTF employs a semi-supervised approach, beginning with pretraining the latent space representations of tactile images and subsequently mapping different tactile image domains to a shared latent space within a simulated tactile image domain. This latent space, combined with the proprioceptive information of the robotic arm, is then integrated into a privileged learning framework for policy training, which results in a deployable tactile control policy. Our results demonstrate the robustness of the proposed framework in achieving task objectives across different tactile sensors with varying physical parameters. Furthermore, manipulators equipped with tactile sensors, allow for rapid training and deployment for diverse contact-rich tasks, including object pushing and surface following.

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NeurIPS Conference 2023 Conference Paper

K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing

Shuai Li
Yingjie Zhang
Hongtu Zhu
Christina Wang
Hai Shu
Ziqi Chen
Zhuoran Sun
Yanfeng Yang

Conditional independence (CI) testing is a fundamental task in statistics and machine learning, but its effectiveness is hindered by the challenges posed by high-dimensional conditioning variables and limited data samples. This article introduces a novel testing approach to address these challenges and enhance control of the type I error while achieving high power under alternative hypotheses. The proposed approach incorporates a computationally efficient classifier-based conditional mutual information (CMI) estimator, capable of capturing intricate dependence structures among variables. To approximate a distribution encoding the null hypothesis, a $k$-nearest-neighbor local sampling strategy is employed. An important advantage of this approach is its ability to operate without assumptions about distribution forms or feature dependencies. Furthermore, it eliminates the need to derive asymptotic null distributions for the estimated CMI and avoids dataset splitting, making it particularly suitable for small datasets. The method presented in this article demonstrates asymptotic control of the type I error and consistency against all alternative hypotheses. Extensive analyses using both synthetic and real data highlight the computational efficiency of the proposed test. Moreover, it outperforms existing state-of-the-art methods in terms of type I and II errors, even in scenarios with high-dimensional conditioning sets. Additionally, the proposed approach exhibits robustness in the presence of heavy-tailed data.

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