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Iberian Robotics Conference (ROBOT)

作者： Daniel Frau-Alfaro Santiago T. Puente Ignacio De Loyola Páez-Ubieta Edison Velasco-Sánchez AUtomatics RObotics and Artificial Vision Lab University Institute for Computer Research San Vicente Spain University Institute for Engineering Research University Miguel Hernández Elche Spain

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Manipulation tasks in robotics usually involve two phases: an approach to the object and the grasp itself. The first action allows the robot to reach a certain pose in space that is likely to allow the object to be manipulated. Reinforcement Learning (RL) techniques allow a policy to be learned through experience given a set of states and actions, so this is a powerful tool for developing controllers for specific tasks such as positioning the robot in a particular point in space. However, when manipulating an object, orientation is as relevant as position. For this reason, a method of RL for positioning the robot's end effector in a suitable position and orientation for manipulation in simulation is presented. This approach models the problem of computing the distance for the reward function using dual quaternions parameterisation, an element that can represent the pose and attitude of a rigid body in Euclidean space in a compact way without having to apply any constraints.

关键词： Training Quaternions Computational modeling Process control Reinforcement learning Aerospace electronics End effectors Trajectory Robots

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Modelling Variations in Newborn Life Support Procedure Using Colored Petri Nets 32nd

Modelling Variations in Newborn Life Support Procedure Using...

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32nd European Safety and Reliability Conference, ESREL 2022

作者： Tan, Alfian Remenyte-Prescott, Rasa Valstar, Michel Sharkey, Don Resilience Engineering Research Group University of Nottingham United Kingdom Industrial Engineering Department Parahyangan Catholic University Indonesia Computer Vision Lab. University of Nottingham United Kingdom Centre for Perinatal Research School of Medicine University of Nottingham United Kingdom

ISBN: (纸本)9789811851834

This research is conducted to study variations in the Newborn Life Support procedure. This procedure follows an evidence-based protocol to resuscitate and stabilize newborn babies requiring assistance at birth. Errors and ineffective actions in this procedure may cause disability or even death of the baby. Therefore, a detailed understanding of variations in this procedure is essential. The Colored Petri Nets approach is applied to model the procedure. Its properties are used to describe the main steps of the procedure, their conditional rules, the required checks, and repetition of necessary steps. A Monte Carlo simulation of the Petri Nets model is used to observe the proportion of babies with unsatisfactory outcome. In-field data and literatures are used to determine the parameter values of the model. Some scenarios with variations relating to the maximum number of trials of respiratory procedure are implemented in the simulation. The results demonstrate that, for example, the higher the maximum limit of the standard ventilation trials is, the lower the proportion of babies with unsatisfactory condition becomes. This type of approach can be utilized to explore additional improvements in the way that the clinical procedure can be performed to enhance newborn care. © 2022 ESREL2022 Organizers. Published by Research Publishing, Singapore.

关键词： Petri nets

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Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

Uncertainty Aware Deep Learning for Fault Prediction Using M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Md Monibor Rahman L. Vidyaratne A. Carpenter C. Tennant K. Iftekharuddin Department of Electrical and Computer Engineering Vision Lab Old Dominion University Norfolk VA USA Jefferson Laboratory Newport News VA USA

The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolutional neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning (ML)-based fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.

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SYNFER: TOWARDS BOOSTING FACIAL EXPRESSION RECOGNITION WITH SYNTHETIC DATA

arXiv

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arXiv 2024年

作者： He, Xilin Luo, Cheng Xian, Xiaole Li, Bing Song, Siyang Khan, Muhammad Haris Xie, Weicheng Shen, Linlin Ge, Zongyuan Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China AIM Lab Faculty of IT Monash University Australia KAUST Saudi Arabia University of Exter United Kingdom MBZUAI United Arab Emirates

Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection. This limitation poses a significant challenge for developing modern deep learning-based facial expression analysis models, particularly foundation models, that rely on large-scale data for optimal performance. To tackle the overarching and complex challenge, we introduce SynFER (Synthesis of Facial Expressions with Refined Control), a novel framework for synthesizing facial expression image data based on high-level textual descriptions as well as more fine-grained and precise control through facial action units. To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique to steer the generation process and a pseudo-label generator to help rectify the facial expression labels for the synthetic images. To demonstrate the generation fidelity and the effectiveness of the synthetic data from SynFER, we conduct extensive experiments on representation learning using both synthetic data and real-world data. Experiment results validate the efficacy of the proposed approach and the synthetic data. Notably, our approach achieves a 67.23% classification accuracy on AffectNet when training solely with synthetic data equivalent to the AffectNet training set size, which increases to 69.84% when scaling up to five times the original size. Our code will be made publicly available. © 2024, CC BY.

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CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying

arXiv

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arXiv 2025年

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang The School of Software Engineering Xi’an Jiaotong University Xi’an China The Tencent AI Lab Shenzhen China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland The Center for Machine Vision and Signal Analysis University of Oulu Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025, CC BY.

关键词： Heart

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Learning Inverse Kinematics Multiplicity of Concentric Tube Robots Using Invertible Neural Networks

Learning Inverse Kinematics Multiplicity of Concentric Tube ...

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International Symposium on Medical Robotics (ISMR)

作者： Paul H. Kang Radian Gondokaryono Majid Roshanfar Robert H. Nguyen Thomas Looi James M. Drake Dale Podolsky Institute of Biomedical Engineering University of Toronto Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention Hospital for Sick Children Toronto Canada Department of Computer Science Medical Computer Vision and Robotics Lab University of Toronto Canada

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Concentric tube robots (CTR) are a promising technology for medical applications due to their small size, flexibility, and ability to make complex shapes. These robots are built from a series of pre-curved, super-elastic tubes that are arranged concentrically and manipulated through rotational and translational movements at their proximal ends. Achieving accurate kinematics is essential in making CTRs useful in minimally invasive surgical procedures where precision and safety is paramount. Due to the difficulty of incorporating nonlinear effects like friction and tube clearances into analytical models, previous works have investigated machine learningbased models for CTR kinematics, leading to higher kinematic accuracies. We present a kinematic model for CTRs using an invertible neural network architecture that, unlike other learningbased models, can generate multiple inverse kinematic solutions. Our model achieved a mean forward kinematic tip error of 2.86 mm ( $\mathbf{3. 4 3 \%}$ normalized to arclength), outperforming a Cosserat rod-based analytical CTR model. In three test cases (two static points and a circle trajectory), our model achieved mean inverse kinematic errors of $6.19,3.33$ , and $3.86 ~\text{mm}(5.66 \%, 4.12 \%$ , and 4.16 % normalized to arclength). We additionally present a robust data capture pipeline that is able to reconstruct the CTR's shape that uses state-of-the-art segmentation models.

关键词： Analytical models Concentric tube robots Accuracy Minimally invasive surgery Shape Neural networks Pipelines Kinematics Trajectory Safety

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Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fan, Shitong Xiao, Feiyang Wang, Wenbo Qi, Shuhan Zhu, Qiaoxi Wang, Wenwu Guan, Jian Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE.

关键词： Acoustic-based traffic monitoring feature fusion graph attention pre-trained model transfer learning

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Image Segmentation in Foundation Model Era: A Survey

arXiv

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arXiv 2024年

作者： Zhou, Tianfei Zhang, Fei Chang, Boyu Wang, Wenguan Yuan, Ye Konukoglu, Ender Cremers, Daniel Department of Computer Science Beijing Institute of Technology China Department of Electronic Engineering Shanghai Jiao Tong University China CCAI Zhejiang University China Computer Vision Lab. ETH Zurich Switzerland The Department of Computer Science Technical University of Munich Germany

Image segmentation is a long-standing challenge in computer vision, studied continuously over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and MaskFormer. With the advent of foundation models (FMs), contemporary segmentation methodologies have embarked on a new epoch by either adapting FMs (e.g., CLIP, Stable Diffusion, DINO) for image segmentation or developing dedicated segmentation foundation models (e.g., SAM, SAM2). These approaches not only deliver superior segmentation performance, but also herald newfound segmentation capabilities previously unseen in deep learning context. However, current research in image segmentation lacks a detailed analysis of distinct characteristics, challenges, and solutions associated with these advancements. This survey seeks to fill this gap by providing a thorough review of cutting-edge research centered around FM-driven image segmentation. We investigate two basic lines of research – generic image segmentation (i.e., semantic segmentation, instance segmentation, panoptic segmentation), and promptable image segmentation (i.e., interactive segmentation, referring segmentation, few-shot segmentation) – by delineating their respective task settings, background concepts, and key challenges. Furthermore, we provide insights into the emergence of segmentation knowledge from FMs like CLIP, Stable Diffusion, and DINO. An exhaustive overview of over 300 segmentation approaches is provided to encapsulate the breadth of current research efforts. Subsequently, we engage in a discussion of open issues and potential avenues for future research. We envisage that this fresh, comprehensive, and systematic survey catalyzes the evolution of advanced image segmentation systems. © 2024, CC BY.

关键词： Semantic Segmentation

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Pipe-BD: Pipelined Parallel Blockwise Distillation

Pipe-BD: Pipelined Parallel Blockwise Distillation

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Design, Automation and Test in Europe Conference and Exhibition

作者： Hongsun Jang Jaewon Jung Jaeyong Song Joonsang Yu Youngsok Kim Jinho Lee Department of Electrical and Computer Engineering Seoul National University Department of Artificial Intelligence Yonsei University Department of Computer Science Yonsei University CLOVA Image Vision CLOVA AI Lab NAVER

Training large deep neural network models is highly challenging due to their tremendous computational and mem-ory requirements. Blockwise distillation provides one promising method towards faster convergence by splitting a large model into multiple smaller models. In state-of-the-art blockwise distillation methods, training is performed block-by-block in a data-parallel manner using multiple GPUs. To produce inputs for the student blocks, the teacher model is executed from the beginning until the current block under training. However, this results in a high overhead of redundant teacher execution, low GPU utilization, and extra data loading. To address these problems, we propose Pipe-BD, a novel parallelization method for blockwise distillation. Pipe-BD aggressively utilizes pipeline parallelism for blockwise distillation, eliminating redundant teacher block execution and increasing per-device batch size for better resource utilization. We also extend to hybrid parallelism for efficient workload balancing. As a result, Pipe-BD achieves significant acceleration without modifying the mathematical formulation of blockwise distillation. We implement Pipe-BD on PyTorch, and experiments reveal that Pipe-BD is effective on multiple scenarios, models, and datasets.

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Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Med-DANet V2: A Flexible Dynamic Architecture for Efficient ...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Haoran Shen Yifu Zhang Wenxuan Wang Chen Chen Jing Liu Shanshan Song Jiangyun Li School of Automation and Electrical Engineering University of Science and Technology Beijing Center for Research in Computer Vision University of Central Florida National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet [44]) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019. Code will be available at https://***/Rubics-Xuan/Med-DANet.

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