Author name cluster

Jianqin Yin

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Exploring Position Encoding Mechanism in Diffusion U-Net for Training-free High-resolution Image Generation

Feng Zhou
Pu Cao
Yiyang Ma
Lu Yang
Yonghao Dang
Jianqin Yin

Denoising higher-resolution latents using a pre-trained U-Net often results in repetitive and disordered image patterns. In this work, we are motivated to reveal the intrinsic cause of such pattern disruption in high-resolution image generation. Through theoretical analysis and empirical studies, we reveal that the pre-trained U-Net fails to provide sufficient positional information for tokens at high-resolution. Specifically, 1) zero-padding serves as a critical mechanism for position encoding but lacks robustness across varying resolutions; and 2) tokens located farther from the feature map boundaries have increasing difficulty acquiring positional awareness, leading to pattern disruptions. Inspired by these findings, we propose a novel training-free approach for high-resolution generation, introducing a Progressive Boundary Complement (PBC) method. It creates dynamic virtual image boundaries inside the feature map to supplement position information at high resolution, enabling high-quality and rich-content high-resolution image synthesis. Extensive experiments show that our method significantly improves high-resolution image synthesis in terms of visual quality and content richness, achieving state-of-the-art performance.

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EAAI Journal 2026 Journal Article

Physical and virtual safety evaluation in autonomous driving systems using three-dimensional adversarial implementations

Yixun Zhang
Jianqin Yin
Yiyang Ma
Yingchun Niu
Xubo Zhang

Ensuring the safety of autonomous driving systems against adversarial attacks is a critical challenge, especially when such attacks occur in the physical world. Existing methods typically focus on assessing safety in isolated images, failing to address the continuous operation of autonomous vehicles under adversarial conditions. To overcome this limitation, this study introduces a comprehensive framework that evaluates both physical and virtual safety to ensure sustained vehicle safety during continuous operation. The framework consists of two key innovations: the three-dimensional (3D) physical implementation of adversarial textures and a safety evaluation framework using novel metrics. The 3D physical implementation provides a robust platform to test the system response to physical threats. The second and primary innovation, the safety evaluation, introduces the Safety Score to quantify physical safety and the Collaborative Efficiency Deterioration Rate (CEDR) to assess virtual safety, with the ultimate objective of improving driving safety under adversarial conditions. Simulated experiments validate the effectiveness of these metrics in identifying security vulnerabilities and provide insights into improving the resilience of autonomous driving technologies. Although the evaluation is conducted in simulation, it is designed to reflect real-world constraints. This research provides a reference for the safety evaluation of automatic driving in practical applications.

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IROS Conference 2025 Conference Paper

3DWSNet: A Novel 3D Wavelet Spiking Neural Network for Event-based Action Recognition

Junkang Fang
Yonghao Dang
Wending Zhao
Bo Yu
Zehao Wang
Jianqin Yin

In robotics applications, event cameras provide low-latency and high-dynamic-range sensing by asynchronously detecting brightness changes, making them well-suited for capturing fast motions and subtle cues in dynamic environments. However, most existing Spiking Neural Network (SNN)-based methods enhance spatial information by stacking multiple frames of events, while neglecting the explicit modeling of high-and low-frequency components in the event stream. To address this limitation, we proposes a 3D Wavelet Spiking Neural Network (3DWSNet), which integrates a 3D wavelet transform with a cascaded Wavelet Spiking Convolution (WSC) module as its core. Specifically, the 3D wavelet transform decomposes input data into eight frequency sub-bands across spatial and temporal dimensions, enabling the model to preserve fine-grained high-frequency details while enriching low-frequency motion representations. The cascaded WSC architecture further improves the extraction of multi-scale spatio-temporal features by integrating information from feature maps at different resolutions. Extensive experiments show that our 3DWSNet significantly outperforms SOTA SNN performances on the CIFAR-10, CIFAR-100, DVS128 Gesture, and CIFAR10-DVS datasets. The source code will be publicly released soon.

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IROS Conference 2025 Conference Paper

AlignCAPE: Support and Query Feature Aligning for Category-Agnostic Pose Estimation

Zhuoran Chen
Jin Tang
Guoliang Xu
Shaojie Zhang
Zhicheng Zhang
Jianqin Yin

Recent advancements in category-agnostic pose estimation have focused on developing a unified model capable of localizing keypoint coordinates across arbitrary categories, which enables robots to accurately interact with diverse objects by understanding their poses. While existing methods predominantly concentrate on local features surrounding the keypoints of the support image, they often overlook the importance of global features, leading to potential misalignment between the support and query image. To address the inherent conflicts between the two images, we propose AlignCAPE, a novel approach designed to mitigate such misalignment and enhance the model performance. Our method formulates a two-stage pipeline, generating initial proposals in the first stage, followed by another stage to refine iteratively. Specifically, we introduce two modules, Feature Alignment Module(FAM) and Keypoint Perception Module(KPM). FAM utilizes bidirectional cross-attention operation to align the support image feature and query image feature, thereby compensating for the limitations of previous methods. KPM employs self-attention mechanism to capture the interactions among keypoints, facilitating to localize keypoints in the query image. Experiments on MP-100 benchmark demonstrate that our method outperforms the widely-used baseline model in CAPE by 0. 68% in PCK@0. 2 metric under 1-shot setting.

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IROS Conference 2025 Conference Paper

MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation

Wei Wei
Shaojie Zhang
Yonghao Dang
Jianqin Yin

Human action recognition is a crucial task for intelligent robotics, particularly within the context of human-robot collaboration research. In self-supervised skeleton-based action recognition, the mask-based reconstruction paradigm learns the spatial structure and motion patterns of the skeleton by masking joints and reconstructing the target from unlabeled data. However, existing methods focus on a limited set of joints and low-order motion patterns, limiting the model’s ability to understand complex motion patterns. To address this issue, we introduce MaskSem, a novel semantic-guided masking method for learning 3D hybrid high-order motion representations. This novel framework leverages Grad-CAM based on relative motion to guide the masking of joints, which can be represented as the most semantically rich temporal orgions. The semantic-guided masking process can encourage the model to explore more discriminative features. Furthermore, we propose using hybrid high-order motion as the reconstruction target, enabling the model to learn multi-order motion patterns. Specifically, low-order motion velocity and high-order motion acceleration are used together as the reconstruction target. This approach offers a more comprehensive description of the dynamic motion process, enhancing the model’s understanding of motion patterns. Experiments on the NTU60, NTU120, and PKU-MMD datasets show that MaskSem, combined with a vanilla transformer, improves skeleton-based action recognition, making it more suitable for applications in human-robot interaction. The source code of our MaskSem is available at https://github.com/JayEason66/MaskSem.

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IROS Conference 2025 Conference Paper

Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation

Meng Chen
Jiawei Tu
Chao Qi
Yonghao Dang
Feng Zhou
Wei Wei
Jianqin Yin

The significant advancements in embodied vision navigation have raised concerns about its susceptibility to adversarial attacks exploiting deep neural networks. Investigating the adversarial robustness of embodied vision navigation is crucial, especially given the threat of 3D physical attacks that could pose risks to human safety. However, existing attack methods for embodied vision navigation often lack physical feasibility due to challenges in transferring digital perturbations into the physical world. Moreover, current physical attacks for object detection struggle to achieve both multi-view effectiveness and visual naturalness in navigation scenarios. To address this, we propose a practical attack method for embodied navigation by attaching adversarial patches to objects, where both opacity and textures are learnable. Specifically, to ensure effectiveness across varying viewpoints, we employ a multi-view optimization strategy based on object-aware sampling, which optimizes the patch’s texture based on feedback from the vision-based perception model used in navigation. To make the patch inconspicuous to human observers, we introduce a two-stage opacity optimization mechanism, in which opacity is fine-tuned after texture optimization. Experimental results demonstrate that our adversarial patches decrease the navigation success rate by an average of 22. 39%, outperforming previous methods in practicality, effectiveness, and naturalness. Code is available at: github.com/chen37058/Physical-Attacks-in-Embodied-Nav.

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JBHI Journal 2024 Journal Article

Deeply Supervised Skin Lesions Diagnosis With Stage and Branch Attention

Wei Dai
Rui Liu
Tianyi Wu
Min Wang
Jianqin Yin
Jun Liu

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin diseases. Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment. Recent studies have reported that ensembled convolutional neural networks (CNNs) are practical to classify the images for early diagnosis of skin disorders. However, the practical use of these ensembled CNNs is limited as these networks are heavyweight and inadequate for processing contextual information. Although lightweight networks (e. g. , MobileNetV3 and EfficientNet) were developed to achieve parameter reduction for implementing deep neural networks on mobile devices, insufficient depth of feature representation restricts the performance. To address the existing limitations, we develop a new lite and effective neural network, namely HierAttn. The HierAttn applies a novel deep supervision strategy to learn the local and global features by using multi-stage and multi-branch attention mechanisms with only one training loss. The efficacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD2020). The experimental results show that HierAttn achieves the best accuracy and area under the curve (AUC) among the state-of-the-art lightweight networks.

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AAAI Conference 2024 Conference Paper

Lifting by Image – Leveraging Image Cues for Accurate 3D Human Pose Estimation

Feng Zhou
Jianqin Yin
Peiyang Li

The "lifting from 2D pose" method has been the dominant approach to 3D Human Pose Estimation (3DHPE) due to the powerful visual analysis ability of 2D pose estimators. Widely known, there exists a depth ambiguity problem when estimating solely from 2D pose, where one 2D pose can be mapped to multiple 3D poses. Intuitively, the rich semantic and texture information in images can contribute to a more accurate "lifting" procedure. Yet, existing research encounters two primary challenges. Firstly, the distribution of image data in 3D motion capture datasets is too narrow because of the laboratorial environment, which leads to poor generalization ability of methods trained with image information. Secondly, effective strategies for leveraging image information are lacking. In this paper, we give new insight into the cause of poor generalization problems and the effectiveness of image features. Based on that, we propose an advanced framework. Specifically, the framework consists of two stages. First, we enable the keypoints to query and select the beneficial features from all image patches. To reduce the keypoints attention to inconsequential background features, we design a novel Pose-guided Transformer Layer, which adaptively limits the updates to unimportant image patches. Then, through a designed Adaptive Feature Selection Module, we prune less significant image patches from the feature map. In the second stage, we allow the keypoints to further emphasize the retained critical image features. This progressive learning approach prevents further training on insignificant image features. Experimental results show that our model achieves state-of-the-art performance on both the Human3.6M dataset and the MPI-INF-3DHP dataset.

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ICRA Conference 2021 Conference Paper

Dynamic tracking for microrobot with active magnetic sensor array

Min Wang 0032
Kwan Yi Leung
Rui Liu 0033
Shuang Song 0002
Yixuan Yuan
Jianqin Yin
Max Q. -H. Meng
Jun Liu 0007

Accurate position feedback in a wide range is critical for medical microrobotics and robot-assisted examinations, such as colonoscopy, bronchoscopy and capsule endoscopy examination. Among the many modalities of positioning feedback, magnetic tracking is a preferable method due to the unique advantages of free line of sight, free energy storage and untethered connection. However, the field strength of the magnetic source decreases with the third power of the distance, limiting the effectiveness of position feedback at long distances. In order to maintain a consistently high tracking accuracy in a broad area, this paper presents a new dynamic tracking solution by applying a movable sensor array. In this new solution, the tracking accuracy of the magnet is first determined and optimized within a short range. When the target microrobot carrying the magnet exceeds this optimized range, the sensor array is relocated by an external robotic arm to keep the target in the effective tracking range. Moreover, we also propose a multi-point locating algorithm to minimize the varying background noise. Experimental results show that the proposed method increases the range of magnetic tracking and achieves a satisfactory level of tracking accuracy, which demonstrates significant potentials to improve the position feedback of microrobots in medical applications.

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ICRA Conference 2021 Conference Paper

Neighborhood Spatial Aggregation based Efficient Uncertainty Estimation for Point Cloud Semantic Segmentation

Chao Qi
Jianqin Yin
Huaping Liu 0001
Jun Liu 0007

Uncertainty estimation for point cloud semantic segmentation is to quantify the confidence degree for the predicted label of points, which is essential for decision-making tasks. This paper proposes a neighborhood spatial aggregation based method, NSA-MC dropout, to achieve efficient uncertainty estimation for point cloud semantic segmentation. Unlike the traditional uncertainty estimation method MC dropout de-pending on repeated inferences, our NSA-MC dropout achieves uncertainty estimation through one-time inference. Specifically, a space-dependent method is designed to sample the model many times by performing stochastic forward pass through the model just once, and it approximates the repeated inferences based sampling process in MC dropout. Besides, a neighborhood spatial aggregation module, called NSA, aggregates neighborhood probabilistic outputs for each point and works with space-dependent sampling to establish output distribution. Finally, we propose an uncertainty-aware framework NSA-MC dropout to capture the uncertainty of prediction results efficiently. Experimental results show that our method obtains comparable performance with MC dropout. More significantly, our NSA-MC dropout has little influence on the efficiency of semantic inference. It is much faster than MC dropout, and the inference time does not establish a coupling relation with the sampling times. Our code is available at https://github.com/chaoqi7/Uncertainty_Estimation_PCSS

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