Author name cluster

Gang Yang

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10 papers

2 author rows

JBHI Journal 2026 Journal Article

Prior-Guided Selective Parameter Fine-Tuning for Source-Free Domain Adaptive Medical Image Segmentation

Fanzhe Yan
Gang Yang
Xun Chen
Yue Yu
Aiping Liu

Source-free domain adaptation (SFDA) transfers knowledge from pre-trained source models to the un labeled target domain without accessing the private source data. Conventional SFDA methods for medical imageseg mentation typically depend on pseudo-label driven self training with full model fine-tuning. Although these methods have shown decent performance, the underlying principles remain insufficiently explored. In this work, we investigate SFDA through the PAC-Bayesian generalization error bound, demonstrating that its generalization error is jointly constrained by model complexity and pseudo-label noise. Motivated by this, we propose PATH, a selective PArameter fine-tuning framework guided by Topological and Historical priors for SFDA medical image segmentation. Specifically, PATH identifies domain-variant and task-distinctive parameters and sparsely updates them, thereby reducing effective model complexity during adaptation. In addition, PATH estimates pseudo-label reliability by integrating topological structure and historical prediction consistency priors to suppress pseudo-label noise. Extensive experiments on cross-scanner fundus image segmentation and cross modality abdominal multi-organ segmentation benchmarks demonstrate that PATH outperforms competing SFDA methods, achieving state-of-the-art performance. Code will be available at https://github.com/dogeONE-bit/PATH.

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ICRA Conference 2024 Conference Paper

Jade: A Differentiable Physics Engine for Articulated Rigid Bodies with Intersection-Free Frictional Contact

Gang Yang
Siyuan Luo
Yunhai Feng
Zhixin Sun
Chenrui Tie
Lin Shao 0002

We present Jade, a differentiable physics engine for articulated rigid bodies. Jade models contacts as the Linear Complementarity Problem (LCP). Compared to existing differentiable simulations, Jade offers features including intersection-free collision simulation and stable LCP solutions for multiple frictional contacts. We use continuous collision detection to detect the time of impact and adopt the backtracking strategy to prevent intersection between bodies with complex geometry shapes. We derive the gradient calculation to ensure the whole simulation process is differentiable under the backtracking mechanism. We modify the popular Dantzig’s algorithm to get valid solutions under multiple frictional contacts. We conduct extensive experiments to demonstrate the effectiveness of our differentiable physics simulation over a variety of contact-rich tasks. Supplemental materials and videos are available on our project webpage at https://sites.google.com/view/diffsim

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AAAI Conference 2024 Conference Paper

Learning Discriminative Noise Guidance for Image Forgery Detection and Localization

Jiaying Zhu
Dong Li
Xueyang Fu
Gang Yang
Jie Huang
Aiping Liu
Zheng-Jun Zha

This study introduces a new method for detecting and localizing image forgery by focusing on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB images carries tampering traces, useful for distinguishing and locating forgeries. However, the advancement of tampering technology complicates the direct application of noise for forgery detection, as the noise inconsistency between forged and authentic regions is not fully exploited. To tackle this, we develop a two-step discriminative noise-guided approach to explicitly enhance the representation and use of noise inconsistencies, thereby fully exploiting noise information to improve the accuracy and robustness of forgery detection. Specifically, we first enhance the noise discriminability of forged regions compared to authentic ones using a de-noising network and a statistics-based constraint. Then, we merge a model-driven guided filtering mechanism with a data-driven attention mechanism to create a learnable and differentiable noise-guided filter. This sophisticated filter allows us to maintain the edges of forged regions learned from the noise. Comprehensive experiments on multiple datasets demonstrate that our method can reliably detect and localize forgeries, surpassing existing state-of-the-art methods.

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IROS Conference 2024 Conference Paper

ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots

Zhixuan Xu
Chongkai Gao
Zixuan Liu 0002
Gang Yang
Chenrui Tie
Haozhuo Zheng
Haoyu Zhou
Weikun Peng

To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatile task-planning ability exhibited by LLMs. The vast diversity in objects, robots, and manipulation tasks presents huge challenges. Our work introduces a comprehensive framework to develop a foundation model for general robotic manipulation that formalizes a manipulation task as contact synthesis. Specifically, our model takes as input object and robot manipulator point clouds, object physical attributes, target motions, and manipulation region masks. It outputs contact points on the object and associated contact forces or post-contact motions for robots to achieve the desired manipulation task. We perform extensive experiments both in the simulation and real-world settings, manipulating articulated rigid objects, rigid objects, and deformable objects that vary in dimensionality, ranging from one-dimensional objects like ropes to two-dimensional objects like cloth and extending to three-dimensional objects such as plasticine. Our model achieves average success rates of around 90%. Supplementary materials and videos are available on our project website at https://manifoundationmodel.github.io/.

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IROS Conference 2024 Conference Paper

SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes

Min Liu
Gang Yang
Siyuan Luo
Lin Shao 0002

Differentiable physics simulation provides an avenue to tackle previously intractable challenges through gradient-based optimization, thereby greatly improving the efficiency of solving robotics-related problems. To apply differentiable simulation in diverse robotic manipulation scenarios, a key challenge is to integrate various materials in a unified framework. We present SoftMAC, a differentiable simulation framework that couples soft bodies with articulated rigid bodies and clothes. SoftMAC simulates soft bodies with the continuum-mechanics-based Material Point Method (MPM). We provide a novel forecast-based contact model for MPM, which effectively reduces penetration without introducing other artifacts like unnatural rebound. To couple MPM particles with deformable and non-volumetric clothes meshes, we also propose a penetration tracing algorithm that reconstructs the signed distance field in local area. Diverging from previous works, SoftMAC simulates the complete dynamics of each modality and incorporates them into a cohesive system with an explicit and differentiable coupling mechanism. The feature empowers SoftMAC to handle a broader spectrum of interactions, such as soft bodies serving as manipulators and engaging with underactuated systems. We conducted comprehensive experiments to validate the effectiveness and accuracy of the proposed differentiable pipeline in downstream robotic manipulation applications. Supplementary materials are available on our project website at https://damianliumin.github.io/SoftMAC.

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I&C Journal 2024 Journal Article

The number of spanning trees for Sierpiński graphs and data center networks

Xiaojuan Zhang
Gang Yang
Changxiang He
Ralf Klasing
Yaping Mao

The number of spanning trees is an important graph invariant related to different topological and dynamic properties of the graph, such as its reliability, synchronization capability and diffusion properties. In 2007, Chang et al. proposed two conjectures on the number of spanning trees of Sierpiński triangle graphs and its spanning tree entropy. In this paper, we completely confirm these conjectures. For data center networks D k, n, we get the exact formula for k = 1, and upper and lower bounds for k ≥ 2. Our results allow also the calculation of the spanning tree entropy of Sierpiński graphs and data center networks.

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AAAI Conference 2024 Conference Paper

TMFormer: Token Merging Transformer for Brain Tumor Segmentation with Missing Modalities

Zheyu Zhang
Gang Yang
Yueyi Zhang
Huanjing Yue
Aiping Liu
Yunwei Ou
Jian Gong
Xiaoyan Sun

Numerous techniques excel in brain tumor segmentation using multi-modal magnetic resonance imaging (MRI) sequences, delivering exceptional results. However, the prevalent absence of modalities in clinical scenarios hampers performance. Current approaches frequently resort to zero maps as substitutes for missing modalities, inadvertently introducing feature bias and redundant computations. To address these issues, we present the Token Merging transFormer (TMFormer) for robust brain tumor segmentation with missing modalities. TMFormer tackles these challenges by extracting and merging accessible modalities into more compact token sequences. The architecture comprises two core components: the Uni-modal Token Merging Block (UMB) and the Multi-modal Token Merging Block (MMB). The UMB enhances individual modality representation by adaptively consolidating spatially redundant tokens within and outside tumor-related regions, thereby refining token sequences for augmented representational capacity. Meanwhile, the MMB mitigates multi-modal feature fusion bias, exclusively leveraging tokens from present modalities and merging them into a unified multi-modal representation to accommodate varying modality combinations. Extensive experimental results on the BraTS 2018 and 2020 datasets demonstrate the superiority and efficacy of TMFormer compared to state-of-the-art methods when dealing with missing modalities.

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IROS Conference 2023 Conference Paper

DiffClothAI: Differentiable Cloth Simulation with Intersection-free Frictional Contact and Differentiable Two-Way Coupling with Articulated Rigid Bodies

Xinyuan Yu
Siheng Zhao
Siyuan Luo
Gang Yang
Lin Shao 0002

Differentiable Simulations have recently proven useful for various robotic manipulation tasks, including cloth manipulation. In robotic cloth simulation, it is crucial to maintain intersection-free properties. We present DiffClothAI, a differentiable cloth simulation with intersection-free friction contact and two-way coupling with articulated rigid bodies. DiffClothAI integrates the Project Dynamics and Incremental Potential Contact coherently and proposes an effective method to derive gradients in the Cloth Simulation. It also establishes the differentiable coupling mechanism between articulated rigid bodies and cloth. We conduct a comprehensive evaluation of DiffClothAI's effectiveness and accuracy and perform a variety of experiments in downstream robotic manipulation tasks. Supplemental materials and videos are available on our project webpage at https://sites.google.com/view/diffsimcloth.

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NeurIPS Conference 2023 Conference Paper

Transition-constant Normalization for Image Enhancement

Jie Huang
Man Zhou
Jinghao Zhang
Gang Yang
Mingde Yao
Chongyi Li
Zhiwei Xiong
Feng Zhao

Normalization techniques that capture image style by statistical representation have become a popular component in deep neural networks. Although image enhancement can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance. To fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks. Specifically, it consists of two streams of normalization operations arranged under an invertible constraint, along with a feature sub-sampling operation that satisfies the normalization constraint. TCN enjoys several merits, including being parameter-free, plug-and-play, and incurring no additional computational costs. We provide various formats to utilize TCN for image enhancement, including seamless integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures. Through extensive experiments on multiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements. Besides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation. The code is available at \textit{\textcolor{blue}{https: //github. com/huangkevinj/TCNorm}}.

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NeurIPS Conference 2021 Conference Paper

Unfolding Taylor's Approximations for Image Restoration

Man Zhou
Xueyang Fu
Zeyu Xiao
Gang Yang
Aiping Liu
Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image. In practice, however, existing methods empirically construct encapsulated end-to-end mapping networks without deepening into the rationality, and neglect the intrinsic prior knowledge of restoration task. To solve the above problems, inspired by Taylor’s Approximations, we unfold Taylor’s Formula to construct a novel framework for image restoration. We find the main part and the derivative part of Taylor’s Approximations take the same effect as the two competing goals of high-level contextualized information and spatial details of image restoration respectively. Specifically, our framework consists of two steps, which are correspondingly responsible for the mapping and derivative functions. The former first learns the high-level contextualized information and the later combines it with the degraded input to progressively recover local high-order spatial details. Our proposed framework is orthogonal to existing methods and thus can be easily integrated with them for further improvement, and extensive experiments demonstrate the effectiveness and scalability of our proposed framework.

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