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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panpan Zhang Haibo Luo Zheng Xu Miao He Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China

ISBN: (纸本)9781665465373

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset.

关键词： Deep learning Training Redundancy Radar polarimetry Inference algorithms Indexes Marine vehicles

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Robust Simultaneous Tracking and Local Dense Structured Mapping at Scenes lack of Geometric Features

Robust Simultaneous Tracking and Local Dense Structured Mapp...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yueling Shen Zhijian Qiao Xu Wang Hesheng Wang Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665431545

In this paper, we present a simultaneous tracking and dense structured mapping method aimed at robotic tasks including obstacle avoidance, path planning and manipulation. This is achieved through (1) sparse-to-dense pose estimation with both keypoints and dense vertex images for keeping robust at scenes lack of geometric features, (2) dense local map algorithm, which clears map outside the neighborhood of the robot for the consistency of the system at large-scale scenes, (3) structured analysis to obtain elevation and norms of surfaces, (4) the proposed dynamic mode for ghosting-free mapping in dynamic scenes. Experiments are conducted to evaluate each part of the system. The effectiveness and efficiency are proved by quantitative experiments.

关键词： Meters Surface reconstruction Three-dimensional displays Mechatronics Heuristic algorithms Pose estimation Path planning

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control Selection Based on State Change of Chaotic System 8

Control Selection Based on State Change of Chaotic System

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhou, Jiaxi Zhang, Wei Feng, Yuming Onasanya, B.O. School of Electronics and Information Engineering Chongqing Three Gorges University Chongqing Wanzhou404100 China Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control Chongqing Wanzhou404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing Wanzhou404100 China University of Ibadan Department of Mathematics Oyo State Ibadan200284 Nigeria

ISBN: (纸本)9781665450515

Considering that the system may need to meet more needs in the control process, that is, to accelerate or slow down the control intensity according to the system state, the control mode that combines the control cycle and the control method has a wider range of applications. In this paper, the model for state detection and judgment in the second half of the control cycle is studied, and appropriate measures are taken to achieve various control effects of the system. The simulation of the application example illustrates the effectiveness of the controller. © 2022 IEEE.

关键词： Chaotic systems

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Unsupervised Learning of 3D Scene Flow from Monocular Camera^*

Unsupervised Learning of 3D Scene Flow from Monocular Camera...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Guangming Wang Xiaoyu Tian Ruiqi Ding Hesheng Wang Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai China

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://***/IRMVLab/3DUnMonoFlow.

关键词： Training Three-dimensional displays Laser radar Laplace equations Pose estimation Training data Cameras

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Attribute-specific control units in StyleGAN for fine-grained image manipulation

arXiv

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arXiv 2021年

作者： Wang, Rui Chen, Jian Yu, Gang Sun, Li Yu, Changqian Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Shanghai China East China Normal University Shanghai China

image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i.e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles. Specifically, we collaboratively manipulate the modulation style channels and feature maps in control units rather than individual ones to obtain the semantic and spatial disentangled controls. Furthermore, we propose a simple yet effective method to detect the attribute-specific control units. We move the modulation style along a specific sparse direction vector and replace the filter-wise styles used to compute the feature maps to manipulate these control units. We evaluate our proposed method in various face attribute manipulation tasks. Extensive qualitative and quantitative results demonstrate that our proposed method performs favorably against the state-of-the-art methods. The manipulation results of real images further show the effectiveness of our method. © 2021, CC BY.

关键词： Modulation

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Dual-Stream Fusion Network With Multi-Head Self-Attention for Multi-Modal Fake News Detection

SSRN

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SSRN 2024年

作者： Yang, Yimei Liu, Jinping Yang, Yujun Cen, Lihui School of Mathematics and Statistics Hunan Normal University Changsha410081 China Huaihua University Hunan Huaihua418000 China College of Information Science and Engineering Hunan Normal University Changsha China School of Automation Central South University Changsha410083 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

With the rapid development of social media platforms such as Weibo, WeChat, and other new media, as well as the rise of deepfake technologies, the identification and containment of fake information have posed significant challenges, becoming one of the key challenges faced by society and governmental institutions. Therefore, the development of intelligent and efficient methods for detecting fake news has become a top priority in combating misinformation. Addressing challenges in current fake news detection techniques, such as cross-lingual issues, data imbalance, and multimodal information fusion, this paper aims to propose a dual-stream fusion network model (DSF-MHSA) based on deep learning for detecting fake news across web pages, images, and text. The model combines the deep learning models ERNIE-M, AlexNet, ShuffleNet, and three multi-head self-attention mechanisms to extract and classify features from multilingual text and images. By utilizing two separate multi-head self-attention mechanisms to process extracted textual and image information, the model captures long-range dependencies among texts to better understand contextual information and captures global information among images to enhance object recognition and localization capabilities. Finally, a single multi-head self-attention mechanism is employed to integrate textual and image information to capture cross-modal correlations, thereby strengthening the model's understanding and identification capabilities of news content. This dual-stream fusion network architecture ensures the model's ability to detect multimodal news scenarios in both English and Chinese short texts and images. Experimental evaluations conducted on three publicly available datasets - Twitter, Weibo, and IKCEST-2023 dataset, which contains real online news data collected from domestic and international social media platforms - demonstrate superior performance of the proposed model in news detection tasks, achieving an accuracy rate

关键词： Network architecture

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Chinese Summary Generation Algorithm Based on Contrastive Attention Mechanism

Chinese Summary Generation Algorithm Based on Contrastive At...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Liupeng Guo Yannan Xiao Yayuan Wen Peizhi Wen School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Electronic and Information Engineering/School of Integrated Circuits GUANGXI Normal University Guilin China

Generative summarization based on the seq2seq model has gained widespread adoption due to the advancements and efficacy of the sequence-to-sequence framework. However, these models encounter challenges in handling out-of-vocabulary words and may produce summaries with factual inaccuracies. This paper addresses these issues by proposing a summarization generation model, MBC, which is built upon the Transformer as the baseline model. Additionally, it incorporates the BERT pre-trained model, pointer generation network, contrastive attention mechanism, and coverage loss. The model employs a pointer generation network to augment its vocabulary coverage capabilities by generating new terms. To enhance the representation of key information from the source text, a contrastive attention mechanism is employed during summary generation. In addition, by introducing coverage loss, the model can reduce repetitive content in the summary. To assess the model’s performance, this paper employs ROUGE values as evaluation metrics and conducts comparative experiments with other models using the LCSTS dataset. The experimental results demonstrate that, in comparison with other models, this model is more adept at attending to salient information within the source text and generating summaries of greater accuracy.

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SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION

SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATI...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wu, Jiawei Fan, Haoyi Zhang, Xiaoqing Lin, Shouying Li, Zuoyong College of Mecanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665438643

In this paper, we propose a novel entropy minimization based semi-supervised method for semantic segmentation. Entropy minimization has proven to be an effective semi-supervised method for realizing the cluster assumption, where the decision boundary should lie in low-density regions. Inspired by the existing consistency training semi-supervised segmentation networks with encoder-decoder architecture, we found that there tend to be more large gradient values at the object edges than other positions in the feature map of the encoder, and therefore propose a feature gradient map regularization to enlarge inter-class distance in the feature space for low-entropy of segmentation prediction. Additionally, we introduce an adaptive sharpening scheme with aleatoric uncertainty, and a class consistency constraint regularization, to alleviate the interference of noise with pseudo labels. Extensive experiments on PASCAL VOC, PASCAL-Context, and Leukocyte datasets show that the proposed method achieves state-of-the-art semi-supervised semantic segmentation performance without almost additional calculations and network structures. © 2021 IEEE Computer Society. All rights reserved.

关键词： Semantics

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Automatic Identification for Projector Brand and Model Number 1

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Zuoyong Wang, Weice Zhang, Fuquan Fan, Haoyi Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

The projector production process needs to pack manufactured projectors. The key step of projector packing is to check whether the brand and model number of the projector is correct or not, for avoiding the projector’s packing error. To achieve automation of identifying projector brand and model number for improving the efficiency of the projector packing, we proposed an automatic identification method by using image processing, character recognition, and string matching. Specifically, image grayscale processing, image gradient calculation, morphological operations, and image thresholding are sequentially performed to determine candidate target regions located by a projector's brand and model number. Then, two shape prior-based image features are designed to perform target region screening, i.e., excluding fake target regions as far as possible. Next, the character recognition technology is used to extract the texts in the target regions. Finally, string matching is performed to obtain identification results of the projector's brand and model number. Experimental results on several projectors demonstrate the effectiveness of the proposed method on automatic identification of projector brand and model number. The proposed method provides a general framework for identifying the brand and model number of other similar electronic products. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Human-AI Collaboration: A Study on Anti-ChatGPT Strategies Employed in Innovative Practical Homework Towards "One-Click-Answer" Issue in AIGC 18th

Human-AI Collaboration: A Study on Anti-ChatGPT Strategies E...

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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Yiwen, Liu Yahui, Yang Jinrong, Fu Tao, Feng Ting, Yin Yingxi, Xiang Yanxia, Gao Taiguo, Qu Xian, Zhang School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China School of Foreign Languages Huaihua University Huaihua418000 China Furong School of Xinhong Dong Autonomous County Huaihua418000 China School of Fine Arts and Design Huaihua University Huaihua418000 China

ISBN: (纸本)9789819707362

ChatGPT as a representative of Artificial Intelligence in Generative Computing (AIGC) has had an impact on higher education. It promotes "human-computer collaboration" in coursework, showing both positive and negative effects. ChatGPT has the potential to provide students with personalized learning support. However, students may become too reliant on these tools and lack independent thinking. Aiming at the above problems, this paper introduces the idea of practical homework resource construction, and provides a series of anti-ChatGPT strategies, which can prevent students ChatGPT’s "one-click answer" from the source. By comparing activities such as teaching effectiveness, questionnaires, and talkback interviews conducted in the pilot course, the data illustrate the effectiveness of the methodology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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