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IEEE Conference on Wireless Communications and Networking

作者： Kai Guo Yilin Xiao Xiaozhen Lu Liang Xiao Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Peng Cheng Laboratory Shenzhen China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Information and Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Deploying high-complexity deep neural network (DNN) on mobile devices presents significant challenges, stemming from the conflict between their computationally intensive demands and the constrained computational resources. Device-to-device ( $D$ 2 $D$ ) assisted mobile edge computing (MEC) exploits sharing resources among devices to perform DNN inference collaboratively for higher efficiency. However, most existing collaborative inference schemes ignore the structure of DNN models, thus suffering from high latency under dynamic network conditions and computational loads. In this paper, we propose a learning-based low-latency collaborative inference scheme for multi-branch models, which schedules the computations of each DNN layer to optimize the alignment between the DNN structure's characteristics and the D2D network environments. Specifically, the computational task of each branch is assigned to nearby mobile devices or the edge server for efficient collaboration. In addition, we design proximal policy optimization with task-specific architecture to choose the computational scheduling policy, which contains a two-stream NN to extract features from the D2D network and the tasks at each layer, as well as a multi-head output to obtain the collaborative devices for all tasks in the layer. Simulation results show that the proposed scheme reduces the inference latency compared with benchmarks.

关键词： Schedules Processor scheduling Computational modeling Simulation Collaboration artificial neural networks Mobile handsets Device-to-device communication Servers Low latency communication

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IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via vision Transformer

arXiv

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arXiv 2024年

作者： Qiu, Yuhang Chen, Honghui Dong, Xingbo Lin, Zheng Liao, Iman Yi Tistarelli, Massimo Jin, Zhe The Anhui Provincial Key Laboratory of Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230093 China The Faculty of Engineering Monash University Wellington Road ClaytonVIC3800 Australia The Department of Physics and Information Engineering Fuzhou University Fuzhou350108 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Computer Science University of Nottingham Malaysia Campus Semenyih43500 Malaysia The Computer Vision Laboratory University of Sassari Sassari07100 Italy

Determining dense feature points on fingerprints used in constructing deep fixed-length representations for accurate matching, particularly at the pixel level, is of significant interest. To explore the interpretability of fingerprint matching, we propose a multi-stage interpretable fingerprint matching network, namely Interpretable Fixed-length Representation for Fingerprint Matching via vision Transformer (IFViT), which consists of two primary modules. The first module, an interpretable dense registration module, establishes a vision Transformer (ViT)-based Siamese Network to capture long-range dependencies and the global context in fingerprint pairs. It provides interpretable dense pixel-wise correspondences of feature points for fingerprint alignment and enhances the interpretability in the subsequent matching stage. The second module takes into account both local and global representations of the aligned fingerprint pair to achieve an interpretable fixed-length representation extraction and matching. It employs the ViTs trained in the first module with the additional fully connected layer and retrains them to simultaneously produce the discriminative fixed-length representation and interpretable dense pixel-wise correspondences of feature points. Extensive experimental results on diverse publicly available fingerprint databases demonstrate that the proposed framework not only exhibits superior performance on dense registration and matching but also significantly promotes the interpretability in deep fixed-length representations-based fingerprint matching. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Sim-to-Real for Soft Robots using Differentiable FEM: Recipes for Meshing, Damping, and Actuation

arXiv

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arXiv 2022年

作者： Dubied, Mathieu Michelis, Mike Y. Spielberg, Andrew Katzschmann, Robert K. Soft Robotics Lab. Department of Mechanical and Process Engineering ETH Zurich Switzerland Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States

An accurate, physically-based, and differentiable model of soft robots can unlock downstream applications in optimal control. The Finite Element Method (FEM) is an expressive approach for modeling highly deformable structures such as dynamic, elastomeric soft robots. In this paper, we compare virtual robot models simulated using differentiable FEM with measurements from their physical counterparts. In particular, we examine several soft structures with different morphologies: a clamped soft beam under external force, a pneumatically actuated soft robotic arm, and a soft robotic fish tail. We benchmark and analyze different meshing resolutions and elements (tetrahedra and hexahedra), numerical damping, and the efficacy of differentiability for parameter calibration using a simulator based on the fast Differentiable Projective Dynamics (DiffPD). We also advance FEM modeling in application to soft robotics by proposing a predictive model for pneumatic soft robotic actuation. Through our recipes and case studies, we provide strategies and algorithms for matching real-world physics in simulation, making FEM useful for soft robots. © 2022, CC BY.

关键词： Finite element method

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A Toolkit to Generate Social Navigation Datasets 21st

A Toolkit to Generate Social Navigation Datasets

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21st International Workshop of Physical Agents, WAF 2020

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom

ISBN: (纸本)9783030625788

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Navigation

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Delving into the Scale Variance Problem in Object Detection

arXiv

引用

arXiv 2022年

作者： Chen, Junliang Zhao, Xiaodong Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

Object detection has made substantial progress in the last decade, due to the capability of convolution in extracting local context of objects. However, the scales of objects are diverse and current convolution can only process single-scale input. The capability of traditional convolution with a fixed receptive field in dealing with such a scale variance problem, is thus limited. Multi-scale feature representation has been proven to be an effective way to mitigate the scale variance problem. Recent researches mainly adopt partial connection with certain scales, or aggregate features from all scales and focus on the global information across the scales. However, the information across spatial and depth dimensions is ignored. Inspired by this, we propose the multi-scale convolution (MSConv) to handle this problem. Taking into consideration scale, spatial and depth information at the same time, MSConv is able to process multi-scale input more comprehensively. MSConv is effective and computationally efficient, with only a small increase of computational cost. For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2.5% improvement in AP (on COCO 2017 dataset), with only 3% increase of FLOPs. MSConv is also flexible and effective for two-stage object detectors. When extended to the mainstream two-stage object detectors, MSConv can bring up to 3.0% improvement in AP. Our best model under single-scale testing achieves 48.9% AP on COCO 2017 test-dev split, which surpasses many state-of-the-art methods. © 2022, CC BY.

关键词： Object detection

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Selective Multi-Scale Learning for Object Detection

arXiv

引用

arXiv 2022年

作者： Chen, Junliang Lu, Weizeng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8% improvement in AP (from 39.1% to 40.9%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0% improvement in AP. © 2022, CC BY.

关键词： Object detection

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A Multimodal Soft Gripper with Variable Stiffness and Variable Gripping Range Based on MASH Actuator

arXiv

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arXiv 2024年

作者： Li, Dannuo Zhou, Xuanyi Xiong, Quan Yeow, Chen-Hua The Department of Biomedical Engineering and Advanced Robotics Centre National University of Singapore Singapore117583 Singapore The Department of Biomedical Engineering National University of Singapore Singapore117583 Singapore The Department of Biomedical Engineering and Advanced Robotics Centre National University of Singapore Singapore117583 Singapore The Department of Biomedical Engineering and Advanced Robotics Centre National University of Singapore Singapore117583 Singapore Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology 02139 United States

Soft pneumatic actuators with integrated strain-limiting layers have emerged as predominant components in the field of soft gripper technology for several decades. However, owing to their intrinsic strain-limiting layer design, these soft grippers possess a singular gripping functionality, rendering them incapable of adapting to diverse gripping tasks with different strategies. Based on our previous work, we introduce a novel soft gripper that offers variable stiffness, an adjustable gripping range, and multifunctionality. The MASH actuator-based soft gripper can expand its gripping range up to threefold compared to the original configuration and ensures secure grip by enhancing stiffness when handling heavy objects. Moreover, it supports multitasking gripping through specific gripping strategy control. Copyright © 2024, The Authors. All rights reserved.

关键词： Pneumatic actuators

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Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs

arXiv

引用

arXiv 2024年

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. © 2024, CC BY.

关键词： Graph neural networks

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Self-Ensembling Depth Completion Via Density-Aware Consistency

SSRN

引用

SSRN 2023年

作者： Zhang, Xuanmeng Zheng, Zhedong Jiang, Minyue Ye, Xiaoqing The ReLER Laboratory Australian Artificial Intelligence Institute University of Technology SydneyNSW2007 Australia The Sea-NExT Joint Lab The Department of Computer Science School of Computing National University of Singapore Singapore117417 Singapore The Department of Computer Vision Technology Baidu Inc. Beijing100085 China

Depth completion can predict a dense depth map by taking a sparse depth map and the aligned RGB image as input, but the acquisition of ground truth annotations is labor-intensive and non-scalable. Therefore, we resort to semi-supervised learning, where we only need to annotate a few images and leverage massive unlabeled data without ground truth labels to facilitate model learning. In this paper, we propose SEED, a SElf-Ensembling Depth completion framework to enhance the generalization of the model on unlabeled data. Specifically, SEED contains a pair of the teacher and student models, which are given high-density and low-density sparse depth maps as input respectively. The main idea underpinning SEED is to enforce the density-aware consistency by encouraging consistent prediction across different-density input depth maps. One empirical challenge is that the pseudodepth labels produced by the teacher model inevitably contain wrong depth values, which would mislead the convergence of the student model. To resist the noisy labels, we propose an automatic method to measure the reliability of the generated pseudo-depth labels adaptively. By leveraging the discrepancy of prediction distributions, we model the pixel-wise uncertainty map as the prediction variance and rectify the training process from noisy labels explicitly. To our knowledge, we are among the early semi-supervised attempts on the depth completion task. Extensive experiments on both outdoor and indoor datasets demonstrate that SEED consistently improves the performance of the baseline model by a large margin and even is on par with several fully-supervised methods. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

引用

Annual IEEE Symposium on computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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