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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

Computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation

Pegasus Simulator: An Isaac Sim Framework for Multiple Aeria...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Marcelo Jacinto João Pinto Jay Patrikar John Keller Rita Cunha Sebastian Scherer António Pascoal Laboratory of Robotics and Engineering Systems ISR/IST University of Lisbon Portugal Robotics Institute Carnegie Mellon University USA

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Developing and testing novel control and motion planning algorithms for aerial vehicles can be a challenging task, with the robotics community relying more than ever on 3D simulation technologies to evaluate the performance of new algorithms in a variety of conditions and environments. In this work, we introduce the Pegasus Simulator, a modular framework implemented as an NVIDIA ® Isaac Sim extension that enables real-time simulation of multiple multirotor vehicles in photo-realistic environments, while providing out-of-the-box integration with the widely adopted PX4-Autopilot and ROS2 through its modular implementation and intuitive graphical user interface. To demonstrate some of its capabilities, a nonlinear controller was implemented and simulation results for two drones performing aggressive flight maneuvers are presented. Code and documentation for this framework are also provided as supplementary material.

关键词： Three-dimensional displays Simulation Robot sensing systems Real-time systems Sensor systems Sensors Task analysis

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Human Detection Combining RGB and Thermal Modalities

Human Detection Combining RGB and Thermal Modalities

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Chaymaa Fakrane Kennedy O. S. Mota Cristiano Premebida Rui P. Rocha Dept. of Electrical and Computer Engineering (DEEC) Institute of Systems and Robotics (ISR) University of Coimbra Portugal

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper addresses a late-fusion approach that combines RGB and thermal (long-wave IR) modalities to enhance human detection in indoor environments for robotic perception domains. The study explores multimodal human detection using YOLO series (v5s-6s and v8s-11s models), demonstrating significant improvements in detection accuracy under challenging lighting conditions on a dataset collected by a mobile robot. The use of a Weighted-Mean (WM) as a late-fusion approach demonstrates favorable results over more complex methods such as neural networks or SVM. The WM approach is simple to implement and has a low computational cost. Additionally, it is more interpretable and modular and allows straightforward adjustments to the weights of the YOLO models and thresholds, facilitating adaptation to the target system's specific needs. The experimental results indicate that the implemented late-fusion approach improves the baseline performance in terms of mean average precision.

关键词： YOLO Support vector machines Adaptation models Accuracy Computational modeling Neural networks Lighting Indoor environment Computational efficiency Mobile robots

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Robot Imitation of Polishing Motions by Observing Humans: From Human Non-Verbal Cues to Stable Limit Cycles

Robot Imitation of Polishing Motions by Observing Humans: Fr...

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IEEE International Conference on Development and Learning, ICDL

作者： Nuno Ferreira Duarte José Santos-Victor Vislab Institute for Systems and Robotics (ISR|Lisboa) Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (数字)9798350348552

ISBN: (纸本)9798350348569

Humans and robots are progressing towards a shareable and co-existing workplace environment. To progress in that direction robots must understand how humans operate as well as their intention at each situation, while at the same time, humans must be aware of the robot's capabilities, and in some similarities, the intention of movement. We propose a time-invariant dynamical system with a limit cycle attractor as the model for all circular/ellipse type polishing motions. This approach was tested on a human dataset of polishing trajectories and in a human-robot scenario resulting in fast and accurate reproduction of the observed polishing motions. The human-robot scenario is accomplished with an end-to-end system that allows for real-time human demonstration of a polishing strategy of a table, an optimization solver that finds the best match for the dynamical system, and the optimal limit cycle is executed by the robot to polish the same table. The code and dataset are available at: https://***/NunoDuarte/RobotPolishingDS.

关键词： Accuracy Limit-cycles Robot sensing systems Real-time systems Vectors Trajectory Task analysis

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Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation

arXiv

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arXiv 2023年

作者： Jacinto, Marcelo Pinto, João Patrikar, Jay Keller, John Cunha, Rita Scherer, Sebastian Pascoal, António The Laboratory of Robotics and Engineering Systems ISR/IST University of Lisbon Portugal The Robotics Institute Carnegie Mellon University United States

Developing and testing novel control and motion planning algorithms for aerial vehicles can be a challenging task, with the robotics community relying more than ever on 3D simulation technologies to evaluate the performance of new algorithms in a variety of conditions and environments. In this work, we introduce the Pegasus Simulator, a modular framework implemented as an NVIDIA® Isaac Sim extension that enables real-time simulation of multiple multirotor vehicles in photo-realistic environments, while providing out-of-the-box integration with the widely adopted PX4-Autopilot and ROS2 through its modular implementation and intuitive graphical user interface. To demonstrate some of its capabilities, a nonlinear controller was implemented and simulation results for two drones performing aggressive flight maneuvers are presented. Code and documentation for this framework are also provided as supplementary material. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphical user interfaces

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Physics-Informed Neural Network for Multirotor Slung Load systems Modeling

Physics-Informed Neural Network for Multirotor Slung Load Sy...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Gil Serrano Marcelo Jacinto José Ribeiro-Gomes João Pinto Bruno J. Guerreiro Alexandre Bernardino Rita Cunha Institute for Systems and Robotics (ISR) Laboratory of Robotics and Engineering Systems (LARSyS) Instituto Superior Técnico University of Lisbon Portugal CTS/Uninova NOVA School of Science and Technology (FCT-NOVA) Caparica Portugal

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent advances in aerial robotics have enabled the use of multirotor vehicles for autonomous payload transportation. Resorting only to classical methods to reliably model a quadrotor carrying a cable-slung load poses significant challenges. On the other hand, purely data-driven learning methods do not comply by design with the problem’s physical constraints, especially in states that are not densely represented in training data. In this work, we explore the use of physics-informed neural networks to learn an end-to-end model of the multirotor-slung-load system and, at a given time, estimate a sequence of the future system states. An LSTM encoder-decoder with an attention mechanism is used to capture the dynamics of the system. To guarantee the cohesiveness between the multiple predicted states of the system, we propose the use of a physics-based term in the loss function, which includes a discretized physical model derived from first principles together with slack variables that allow for a small mismatch between expected and predicted values. To train the model, a dataset using a real-world quadrotor carrying a slung load was curated and is made available. Prediction results are presented and corroborate the feasibility of the approach. The proposed method outperforms both the first principles physical model and a comparable neural network model trained without the physics regularization proposed.

关键词： Training Neural networks Transportation Training data Predictive models systems modeling Vehicle dynamics

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Self-Supervised Segmentation of 3D Fluorescence Microscopy Images Using CycleGAN

Self-Supervised Segmentation of 3D Fluorescence Microscopy I...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alice Rosa Hemaxi Narotamo Margarida Silveira Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

In recent years, deep learning models have been extensively applied for the segmentation of microscopy images to efficiently and accurately quantify and characterize cells, nuclei, and other biological structures. However, typically these are supervised models that require large amounts of training data that are manually annotated to create the ground-truth. Since manual annotation of these segmentation masks is difficult and time-consuming, specially in 3D, we sought to develop a self-supervised segmentation *** method is based on an image-to-image translation model, the CycleGAN, which we use to learn the mapping from the fluorescence microscopy images domain to the segmentation domain. We exploit the fact that CycleGAN does not require paired data and train the model using synthetic masks, instead of manually labeled masks. These masks are created automatically based on the approximate shapes and sizes of the nuclei and Golgi, thus manual image segmentation is not needed in our proposed *** experimental results obtained with the proposed CycleGAN model are compared with two well-known supervised segmentation models: 3D U-Net [1] and Vox2Vox [2]. The CycleGAN model led to the following results: Dice coefficient of 78.07% for the nuclei class and 67.73% for the Golgi class with a difference of only 1.4% and 0.61% compared to the best results obtained with the supervised models Vox2Vox and 3D U-Net, respectively. Moreover, training and testing the CycleGAN model is about 5.78 times faster in comparison with the 3D U-Net model. Our results show that without manual annotation effort we can train a model that performs similarly to supervised models for the segmentation of organelles in 3D microscopy *** relevance— Segmentation of cell organelles in microscopy images is an important step to extract several features, such as the morphology, density, size, shape and texture of these organelles. These quantitative analyses provide valuable infor

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Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Tracking and Counting Apples in Orchards Under Intermittent ...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Gonçalo P. Matos Carlos Santiago João P. Costeira Ricardo L. Saldanha Ernesto M. Morgado SISCOG - Sistemas Congitivos SA Lisbon Portugal Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Univ. of Lisbon Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating what will be the fruit yield in an orchard helps farmers to better plan the resources needed for harvesting, storing, and commercialising the crop, and also to take some agricultural decisions (like pruning) that may increase the quality of the yield and increase profits. Therefore, over the last years, several methods based on computer vision were proposed to automate this task, by directly counting the fruits on trees using a video camera. However, existing works and methods usually assume ideal conditions, and may fail under more challenging scenarios with unconstrained camera motion and intermittent occlusions of fruits. Here we show that combining Structure-from-Motion (SfM) with a bipartite graph matching has the potential to address those challenges. We found that our approach applied to real-world datasets, with unconstrained camera motion and low frame rates, outperforms existing methods by a large margin. Our results demonstrate that the proposed method is robust to multiple intermittent occlusions under challenging conditions, and thus suitable to be used in diverse real-world scenarios in orchards, either with a camera operated by hand or mounted on an agricultural vehicle. Although not shown here, we believe that the proposed method can also be applied to other object tracking problems besides counting fruits, under similar settings — i.e. static objects and a freely moving camera.

关键词： Computer vision Visualization Conferences Crops Cameras Trajectory Pattern recognition

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A Holistic Optimization Method for the Electromagnetic Design of Radial-Flux, High-Power-Density Synchronous Reluctance Machines

A Holistic Optimization Method for the Electromagnetic Desig...

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International Conference on Electrical Machines, ICEM

作者： André M. Silva Miguel Aires Filipe Correia Carlos Henggeler Antunes Fernando J. T. E. Ferreira Continental Engineering Services Portugal Unipessoal Lda. Porto Portugal University of Coimbra Institute for Systems Engineering and Computers (INESC) Coimbra Portugal University of Coimbra Institute of Systems and Robotics (ISR-UC) Coimbra Portugal

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

In this paper, a comprehensive, multi-variable, multi-objective, optimization method is proposed for the design of radial-flux synchronous reluctance machines with high efficiency and power density. In the constrained multi-objective optimization problem, a relatively large number of critical parameters of the stator core, rotor core, and stator winding are simultaneously controlled through an evolutionary algorithm. A novel flux barrier shape and position mechanism, based on polynomial functions, is proposed to evaluate a greater number of cross-sectional geometries. The electromagnetic performance of the solutions is assessed with a non-parametric magnetic equivalent circuit method, which allows for fast and accurate calculations. A design optimization example is provided for validation of the proposed method. The selected design has a peak power density of 6.5 kW/kg at 5000 r/min and a peak torque density of 12.4 N. m/kg. The weight of the active parts is 32.8 kg (winding and electrical steel cores) and the volume is 8.1 liters, which can be considered within the published state-of-the-art.

关键词： Stator cores Power system measurements Torque Density measurement Magnetic cores Windings Stator windings Rotors Steel Electromagnetics

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Understanding the Impact of True vs. Positive VR Feedback on EEG Features and BCI Performance

Understanding the Impact of True vs. Positive VR Feedback on...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Shay Englander Bendor Daniela Esteves Madalena Valente Patricia Figueiredo Athanasios Vourvopoulos Institute for Systems and Robotics (ISR-Lisboa) Lisbon Portugal Department of Neuroscience Bar Ilan University (BIU) Ramat Gan Israel Faculdade de Ciências da Universidade de Lisboa Instituto de Biofísica e Engenharia Biomédica Lisbon Portugal Bioengineering Department Institute for Systems and Robotics (ISR-Lisboa) Instituto Superior Técnico (IST) Lisbon Portugal

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, offering significant potential for rehabilitation and assistive technologies. A major challenge is the performance gap between BCI training and real-time control due to the variability of the underlying brain signals measured using EEG and user mental strategies. Virtual Reality (VR) plays a crucial role in BCI training by providing immersive, embodied feedback, enhancing user engagement, and potentially improving performance. This study investigates how closed-loop BCI systems, based on motor imagery (MI) of left and right-hand movements, with different VR feedback modalities-comparing true vs. positive feedback-impact EEG features like Event-Related Desynchronization (ERD) and BCI performance. Fifteen participants performed MI BCI training and control, in a VR environment, designed to induce increased sense of embodiment. Our results show consistent classification accuracy and ERD levels across both feedback conditions, indicating that VR feedback, when embodying a virtual body, whether true or positive, supports stable BCI performance. Additionally, key discriminative features emerged outside conventional ERD regions, highlighting the value of exploring non-traditional EEG features for MI task differentiation. This study underscores the importance of VR in optimizing closed-loop BCI systems and improving understanding of neurophysiological responses during MI tasks.

关键词： Training Solid modeling Accuracy Virtual reality Motors Brain modeling Electroencephalography Real-time systems Brain-computer interfaces Reliability

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