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arXiv 2022年

作者： Li, Yinhao Ge, Zheng Yu, Guanyi Yang, Jinrong Wang, Zengran Shi, Yukang Sun, Jianjian Li, Zeming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China University of Chinese Academy of Sciences China MEGVII Technology China Huazhong University of Science and Technology China Xi’an Jiaotong University China

In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird’s-Eye-View (BEV) 3D object detection. Our work is based on a key observation – depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Code is released at https://***/Megvii-BaseDetection/BEVDepth. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Yolov9-Spe: A Novel Deep Learning Method For Classroom Student Behavior Detection

SSRN

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SSRN 2024年

作者： Liu, Junxiu Wu, Fengxiang Lu, Baoshan Qin, Sheng Fu, Qiang Luo, Yuling Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic Technology Guilin541004 China

Deep learning can significantly enhance student behavior detection in classrooms. However, challenges such as small target recognition, blurry data, occlusion, and multi-scale detection persist. To address these, we propose YOLOv9-SPE, an improved model based on YOLOv9, incorporating Space-to-Depth Convolution (SPD-Conv), Partial Convolution (PConv), and an Efficient Multi-Scale Attention (EMA) Module. SPD-Conv enhances small target recognition and improves feature extraction for blurry data, while PConv, adapted from FasterNet, addresses occlusion issues. EMA is integrated to handle multi-scale targets in classroom student behavior detection. Experimental results on a student behavior dataset show that YOLOv9-SPE achieves a 2.1% improvement in mean average precision over the original YOLOv9. These enhancements provide a more accurate and efficient method for monitoring and analyzing student behavior in educational settings. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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ANAS: Sentence Similarity Calculation Based on Automatic Neural Architecture Search 4th

ANAS: Sentence Similarity Calculation Based on Automatic Neu...

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4th IFIP TC 12 International Conference on Intelligence Science, ICIS 2020

作者： Wang, Dong-Sheng Zhao, Cui-Ping Wang, Qi Ofori, Kwame Dwomoh Han, Bin Liu, Ga-Qiong Wang, Shi Wang, Hua-Yu Zhang, Jing School of Computer Science Jiangsu University of Science of Technology Zhenjiang Jiangsu212001 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Army Engineering University Nanjing Jiangsu210007 China

ISBN: (纸本)9783030748258

Sentence similarity calculation is one of several research topics in natural language processing. It has been widely used in information retrieval, intelligent question answering and other fields. Traditional machine learning methods generally extract sentence features through manually defined feature templates and then perform similarity calculations. This type of method usually requires more human intervention and constantly has to deal with domain migration problems. Automatically extracting sentence features using certain deep learning algorithms helps to solve domain migration problems. The training data is of the domain is required to complete the extraction process. However, the neural network structure design in the deep learning model usually requires experienced experts to carry out multiple rounds of the tuning design phase. Using an automatic neural architecture search (ANAS) technology deals with all the network structure design problems. This paper proposes a sentence similarity calculation method based on neural architecture search. This method uses a combination of grid search and random search. The method in this paper is tested on the Quora "Question-Pairs" data set and has an accuracy of 81.8%. The experimental results show that the method proposed in this paper can efficiently and automatically learn the network structure of the deep learning model to achieve high accuracy. © 2021, IFIP International Federation for information processing.

关键词： Learning algorithms

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KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of Images and AU Features structure for Masked Facial Expression Recognition

KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huijie Gu Hanpu Wang Shiwei He Mengyan Li Jianmeng Zhou Tong Chen College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Generic Technology and System of Service Robots SWU China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Masked Facial Expression refers to facial expression that people deliberately make which do not correspond to their true emotions. The purpose of masked facial expression is to conceal one’s genuine feelings. Precisely identifying masked facial expression aids in revealing the genuine emotions, and finds applications in diagnosis of mental illness. However, the masked facial expression sequences have redundant information, temporal modeling is difficult, and the expressive ability of AU features needs to be improved. To solve the existing problems, we first propose a Key frame Selection and Data Augmentation (KS-DA) method to obtain the key frame in the sequence. In addition, we propose a Global Feature Relation Block (GFR) to aggregate global information and combine it with Temporal Convolutional Networks (TCN) to better realize temporal modeling. Finally, we propose an Adaptive Weight Generation Module (Ada-WGM) to form different weights from adaptation, allowing for better extraction of the temporal changes in AU features. Finally, the experiments have shown that the proposed method has improved the mixed expression classification task (36 task) by 15.23% and the experienced emotions classification task (6E task) by 17.77%, respectively, and obtain sort results.

关键词： Training Gold Image recognition Face recognition Computational modeling Video sequences Feature extraction Data augmentation Computational efficiency Convolutional neural networks

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An Overview of Organs-on-Chips Based on Deep Learning

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Research 2022年第2期2022卷 141-160页

作者： Jintao Li Jie Chen Hua Bai Haiwei Wang Shiping Hao Yang Ding Bo Peng Jing Zhang Lin Li Wei Huang Frontiers Science Center for Flexible Eletronrics Xi'an Institu te of Flexible Electronics(IFE)and Xr'an Institute of Biomedical Materials EngineeringNorthwesterm Polytechnical UniversityXi'an 710072China Key Laboratory of Ietlieant Computing and Signal Processing of Minsry of Educaton School of Electronics and Information EngineeringAnhui UriversityHefei 230601China 38th Research Institute of China Eilectronics Technology Grouep Corporation Hiefei 230088 China Collge of Biomedical Engimeering Sichuan UntiversityChengdu 610065China Key Laboratory of Flexible Elecronics(KLOFE)and Institute of Advanced Materials(IAM)Nanjing Tech University(NanjingTech)J Nanjing 211800 China

Microfluidic-based organs-on-chips(OoCs)are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro *** miniaturization,stimulated tissue mechanical *** microenvironment of OoCs offer unique properties for biomedical ***,the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical *** learning,an emerging area of research in the field of machine learning,can automatically mine the inherent characteristics and laws of"big data"and has achieved remarkable applications in computer vision,speech recognition,and natural language *** integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development,disease modeling,and personalized *** review bricfly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful *** then analyze thecombination of OoCs and deep learning for image digitization,data analysis,and ***,the problems faced in current applications are discussed,and future perspectives and suggestions are provided to further strengthen this integration.

关键词： Deep holds beyond

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Cascading Failures in Interdependent Directed Networks Under Localized Attacks

SSRN

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SSRN 2023年

作者： Lv, Mengyu Pan, Linqiang Liu, Xueming Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Many real-world systems interact with one another through dependency links, which reduces the system robustness. Most previous studies on the robustness of interdependent networks focus on undirected networks, and the related studies on directed networks are limited to random or targeted attacks. However, some failure scenarios can not be described by these two kinds of attacks, such as earthquakes, floods, and epidemics, where systems are attacked in a local range. In this work, we develop a theoretical framework for analyzing the robustness of interdependent directed networks under localized attacks. We find that for degree homogeneous networks, network robustness under localized attacks is similar to that under random attacks. There are four phase transitions in the phase diagram of the network, and a four-phase transition point and a two-phase transition point are found. For degree heterogeneous networks, localized attacks are more likely to lead to collapse than random attacks. As the coupling strength between networks increases, the interdependent networks first show a second order phase transition, and then a hybrid phase transition, and a first order phase transition at last. Furthermore, as the degree heterogeneity decreases, the robustness of networks first increases and then decreases, showing a local robustness maximum. The findings could help understand network robustness and enable better design of robust interdependent systems. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery

arXiv

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arXiv 2024年

作者： Zhang, Wei Cai, Miaoxin Zhang, Tong Lei, Guoqiang Zhuang, Yin Mao, Xuerui The Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China The School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003 China The National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing100081 China

Ship detection needs to identify ship locations from remote sensing (RS) scenes. Due to different imaging payloads, various appearances of ships, and complicated background interference from the bird’s eye view, it is difficult to set up a unified paradigm for achieving multi-source ship detection. To address this challenge, in this article, leveraging the large language models (LLMs)’s powerful generalization ability, a unified visual-language model called Popeye1 is proposed for multi-source ship detection from RS imagery. Specifically, to bridge the interpretation gap between the multi-source images for ship detection, a novel unified labeling paradigm is designed to integrate different visual modalities and the various ship detection ways, i.e., horizontal bounding box (HBB) and oriented bounding box (OBB). Subsequently, the hybrid experts encoder is designed to refine multi-scale visual features, thereby enhancing visual perception. Then, a visual-language alignment method is developed for Popeye to enhance interactive comprehension ability between visual and language content. Furthermore, an instruction adaption mechanism is proposed for transferring the pre-trained visual-language knowledge from the nature scene into the RS domain for multi-source ship detection. In addition, the segment anything model (SAM) is also seamlessly integrated into the proposed Popeye to achieve pixel-level ship segmentation without additional training costs. Finally, extensive experiments are conducted on the newly constructed ship instruction dataset named MMShip, and the results indicate that the proposed Popeye outperforms current specialist, open-vocabulary, and other visual-language models for zero-shot multi-source ship detection. Copyright © 2024, The Authors. All rights reserved.

关键词： Ships

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EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain

arXiv

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arXiv 2024年

作者： Zhang, Wei Cai, Miaoxin Zhang, Tong Zhuang, Yin Mao, Xuerui The Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China The School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003 China The National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing100081 China

Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain. Owing to the significant diversities between the natural and remote sensing (RS) images, the development of MLLMs in the RS domain is still in the infant stage. To fill the gap, a pioneer MLLM named EarthGPT integrating various multi-sensor RS interpretation tasks uniformly is proposed in this paper for universal RS image comprehension. Firstly, a visual-enhanced perception mechanism is constructed to refine and incorporate coarse-scale semantic perception information and fine-scale detailed perception information. Secondly, a cross-modal mutual comprehension approach is proposed, aiming at enhancing the interplay between visual perception and language comprehension and deepening the comprehension of both visual and language content. Finally, a unified instruction tuning method for multi-sensor multi-task in the RS domain is proposed to unify a wide range of tasks including scene classification, image captioning, region-level captioning, visual question answering (VQA), visual grounding, object detection, etc. More importantly, a dataset named MMRS-1M featuring large-scale multi-sensor multi-modal RS instruction-following is constructed, comprising over 1M image-text pairs based on 34 existing diverse RS datasets and including multi-sensor images such as optical, synthetic aperture radar (SAR), and infrared. The MMRS-1M dataset addresses the drawback of MLLMs on RS expert knowledge and stimulates the development of MLLMs in the RS domain. Extensive experiments are conducted, demonstrating the EarthGPT’s superior performance in various RS visual interpretation tasks compared with the other specialist models and MLLMs, proving the effectiveness of the proposed EarthGPT and offering a versatile paradigm for open-set reasoning tasks. Our code and dataset are available at https://***/wivizhang/EarthGPT. Copyright © 202

关键词： Semantics

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Fine-Grained Spatial-Temporal Framework for Engagement Prediction 2nd

Fine-Grained Spatial-Temporal Framework for Engagement Pred...

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2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Wang, Shanshan Zheng, Yong Wang, Keyang Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Beijing China Institutes of Physical Science and Information Technology Anhui University Anhui China Zhejiang Dahua Technology Co. Ltd. Zhejiang China School of Computer Science and Technology Anhui University Anhui China

ISBN: (纸本)9789819650835

The engagement prediction task aims to identify the current level of involvement of individuals based on the information presented in video clips. In recent years, engagement prediction has attracted considerable attention due to its significance in real-life scenarios. We observed that an individual’s engagement status is often primarily manifested in the facial region. This observation prompted us to solve the engagement prediction problem by exploiting facial information to learn features related to engagement. In this paper, we propose a novel model for learning fine-grained features, called Fine-Grained Spatial-Temporal Framework (FGST). Specifically, we consider both spatial and temporal aspects. In the spatial domain, we propose an entropy-weighted method based on Fourier transform. By applying Fourier transform to video frames and introducing the concept of information entropy, we can segregate engagement-related information from engagement-unrelated information and mining difficult samples. This aids in enabling the model to learn features conducive to engagement prediction. In the temporal domain, we calculate the differences between adjacent frames and sum these differences to obtain regions of pixel variation between video frames, thereby generates saliency weight and improving model performance. Experiments on several public datasets demonstrate the proposed model can outperform the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fourier transforms

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A Modeling Method Based on Koopman Operator Theory for Omnidirectional Mobile Manipulator System

A Modeling Method Based on Koopman Operator Theory for Omnid...

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IEEE International Conference on Mechatronics and Automation

作者： Dong Wang Binjie Wang Tong Zhang Chao Ren Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology Tianjin Institute of Aerospace Mechanical and Electrical Equipment Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Jinhang Institute of Computing Technologies Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

The omnidirectional mobile robotic arm system is a highly coupled, multivariate, and highly nonlinear system. The traditional modeling approach uses Lagrange method to establish dynamic model, which suffers from issues of complex modeling, unknown model parameters, and insufficient accuracy in modeling. In this paper, a modeling method based on Koopman operator theory is proposed for this system, which is essentially a data-driven modeling method. Specifically, this paper collects data on system dynamic characteristics and transforms complex nonlinear models into high-dimensional linear models. This paper preprocesses the voltage data in the dataset based on platform characteristics and obtains the Koopman high-dimensional linear model, avoiding the complex modeling problems using Lagrange methods. Simulation has been performed, which confirms the accuracy and effectiveness of using Koopman operator theory for modeling omnidirectional mobile manipulators.

关键词： Accuracy Trajectory tracking System dynamics Simulation Transforms Predictive models Trajectory

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