Author name cluster

Ying Cao

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

CD-DPE: Dual-Prompt Expert Network Based on Convolutional Dictionary Feature Decoupling for Multi-Contrast MRI Super-Resolution

Xianming Gu
Lihui Wang
Ying Cao
Zeyu Deng
Yingfeng Ou
Guodong Hu
Yi Chen

Multi-contrast magnetic resonance imaging (MRI) super-resolution intends to reconstruct high-resolution (HR) images from low-resolution (LR) scans by leveraging structural information present in HR reference images acquired with different contrasts. This technique enhances anatomical detail and soft tissue differentiation, which is vital for early diagnosis and clinical decision-making. However, inherent contrasts disparities between modalities pose fundamental challenges in effectively utilizing reference image textures to guide target image reconstruction, often resulting in suboptimal feature integration. To address this issue, we propose a dual-prompt expert network based on a convolutional dictionary feature decoupling (CD-DPE) strategy for multi-contrast MRI super-resolution. Specifically, we introduce an iterative convolutional dictionary feature decoupling module (CD-FDM) to separate features into cross-contrast and intra-contrast components, thereby reducing redundancy and interference. To fully integrate these features, a novel dual-prompt feature fusion expert module (DP-FFEM) is proposed. This module uses a frequency prompt to guide the selection of relevant reference features for incorporation into the target image, while an adaptive routing prompt determines the optimal method for fusing reference and target features to enhance reconstruction quality. Extensive experiments on public multi-contrast MRI datasets demonstrate that CD-DPE outperforms state-of-the-art methods in reconstructing fine details. Additionally, experiments on unseen datasets demonstrated that CD-DPE exhibits strong generalization capabilities.

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AAAI Conference 2026 Conference Paper

Ordinal Secretaries with Advice

Hasti Nourmohammadi
Ying Cao
Bo Sun
Xiaoqi Tan

We study the ordinal secretary problem, where a sequence of candidates arrives in uniformly random order, and the goal is to select the best candidate using only pairwise comparisons. We consider a learning-augmented setting that incorporates potentially erroneous predictions about the best candidate’s position. Our goal is to design online algorithms that balance robustness against poor predictions while having high performance when predictions are accurate. Using an optimization-based framework, we develop deterministic and randomized algorithms that extend classical strategies and explicitly model the trade-off between consistency and robustness. Also, we show the flexibility of our approach by applying it to multiple secretary problem variants, including multiple-choice and rehiring.

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AAAI Conference 2026 Conference Paper

Uncertainty-Propelled Physics-MAE Fusion for Self-Supervised Diffusion-Weighted Image Denoising

Zeyu Deng
Lihui Wang
Xi Tao
Qijian Chen
Ying Cao
XuLinHu
Yingfeng Ou

The inherently low signal-to-noise ratio (SNR) in diffusion-weighted (DW) imaging fundamentally impedes precise tissue microstructure characterization, rendering effective noise suppression a persistent challenge. Existing denoising methods frequently suffer from over-smoothing or distortion of microstructure information when handling spatially correlated or severe noise. To address these limitations, we propose UP2-MAE fusion model, a self-supervised DWI denoising method based on Uncertainty-Propelled Physics and Masked Auto-Encoder (MAE) fusion. This framework integrates two complementary branches: one leverages MAE to suppress noise through local context modeling, while the other constructs uncorrelated noisy pairs using diffusion tensor imaging (DTI) physics and denoises them via a Noise2Noise approach, which can preserve texture details by exploiting directional relationships across diffusion encoding directions. To fully integrate the strengths of both branches, an uncertainty-propelled fusion strategy based on maximum likelihood estimation is proposed to derive the final denoised output. In addition, to further promote the performance, uncertainty-guided reconstruction and consistency loss are presented. Evaluations against state-of-the-art denoising methods on both simulated and acquired DW datasets confirm the efficacy of our approach.

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IROS Conference 2025 Conference Paper

Haptic Feedback Control Strategy for Microswarm Navigation in Flowing Environments

Ying Cao
Yanjia Yuan
Qijun Yang
Shengming Luo
Xuanyu An
Haoyu Zhang
Jiansheng Du
Xiaoyu Wang

Swarming microrobots offer great promise for targeted delivery in biofluidic environments. However, current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities. This work proposes a real-time navigation and control strategy with haptic feedback for delivering magnetic microswarm, in which the haptic feedback system provides microswarm-environment interaction to the operator. The real-time tracking system continuously monitors the position and shape of the microswarm in the remote environment, transmitting data to the control system for decision-making. This integration can achieve real-time perception and feedback of the microswarm’s state and motion process. Moreover, the strategy successfully demonstrates navigation and shape-adaptive regulation of the microswarm under static, downstream and three-dimensional (3D) upstream flow conditions. The experimental results show that the haptic feedback enables real-time trajectory and velocity adjustments during navigation, improving control robustness and delivery accuracy. Our work expands a haptic feedback-enabled microswarm control in dynamic conditions, providing an adaptive swarm control strategy in complex biomedical environments.

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AAAI Conference 2025 Conference Paper

HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models

Zhifeng Xie
Hao Li
Huiming Ding
Mengtian Li
Xinhan Di
Ying Cao

Fashion design is a challenging and complex process. Recent works on fashion generation and editing are all agnostic of the actual fashion design process, which limits their usage in practice. In this paper, we propose a novel hierarchical diffusion-based framework tailored for fashion design, coined as HieraFashDiff. Our model is designed to mimic the practical fashion design workflow, by unraveling the denosing process into two successive stages: 1) an ideation stage that generates design proposals given high-level concepts and 2) an iteration stage that continuously refines the proposals using low-level attributes. Our model supports fashion design generation and fine-grained local editing in a single framework. To train our model, we contribute a new dataset of full-body fashion images annotated with hierarchical text descriptions. Extensive evaluations show that, as compared to prior approaches, our method can generate fashion designs and edited results with higher fidelity and better prompt adherence, showing its promising potential to augment the practical fashion design workflow.

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IROS Conference 2025 Conference Paper

Long-Distance Delivery of Collective Cell Microrobots Driven by Mobile Magnetic Actuation System

Yimin Sun
Ying Cao
Haoyu Zhang
Bin Wang
Qijun Yang
Mingxue Cai
Tiantian Xu
Qianqian Wang

Collective microrobots enable controlled batch delivery, showing promising application in the biomedical field. However, significant challenges remain in achieving long-distance delivery of collective microrobots in dynamic environments. This study proposes a magnetic actuation strategy for delivering collective cell microrobots in flowing conditions. A magnetic actuation method is developed, and a mobile actuation system with multiple coils coordination is designed to generate spatially isotropic magnetic fields. Experiments of delivering collective microrobots are conducted in flowing conditions, including downstream and upstream with an average flow velocity up to 8. 84 mm/s. Results demonstrate that the proposed actuation strategy enhances driving performance in dynamic environments, achieving long-distance delivery of collective microrobots (over 548 mm). The final access rate of microrobots reaches 90. 63% and 94. 79% in upstream and downstream conditions, respectively. Our strategy provides an efficient control method for delivering collective microrobots, showing potential for targeted delivery in biomedical applications.

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IROS Conference 2025 Conference Paper

Selective Motion Control of Cell Microrobots in Three-Dimensional Space

Haoyu Zhang
Yimin Sun
Xuanyu An
Jiansheng Du
Shengming Luo
Ying Cao
Jiangfan Yu
Qianqian Wang 0003

Magnetic microrobots are showing great potential in micromanipulation due to the capability of motion control under external fields. However, achieving selective control of magnetic microrobots in three-dimensional (3D) space using global magnetic fields still presents a challenge. In this work, we propose a selective control strategy based on a movable electromagnetic coil system, incorporating a mass-spring-damping model to achieve precise control of cell microrobots in 3D space. By combining theoretical analysis with vision-based feedback, experiments are demonstrated in different scenarios, including step climbing and ring traversal, validating the control capability in different environments. Furthermore, by utilizing the differences in magnetic responses among cell microrobots, this strategy enables selective manipulation of multiple cell microrobots, demonstrating real-time sorting manipulation in a 3D space. Our work presents a strategy that can be applied to selectively manipulate magnetic microrobots in complex environments.

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IROS Conference 2022 Conference Paper

Optimal Nonprehensile Interception Strategy for Objects in Flight

Cheng Zhou
Yanbo Long
Ying Cao
Longfei Zhao
Bidan Huang
Yu Zheng 0001

Intercepting an object in flight through nonpre-hensile manipulation is a challenging problem, which is aimed at catching and stopping a flying object using little contacts without completely restraining its relative motion to the robot. This paper presents a two-stage optimal trajectory generation method to tackle this problem. At the pre-catching stage, optimal position and attitude trajectories of the robot's end-effector to approach the object are generated by a variational method. At the post-catching stage, the end-effector's trajectories are generated to optimally eliminate the translational and rotational motion of the object and a convex-MPC algorithm combined with admittance control is used to realize the trajectory tracking. A series of simulations and experiments have been conducted to verify the effectiveness of the proposed method.

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