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Dual Collaborative Visual-Semantic Mapping for Multi-Label Zero-Shot image recognition 48

Dual Collaborative Visual-Semantic Mapping for Multi-Label Z...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Hu, Yunqing Jin, Xuan Chen, Xi Zhang, Yin Zhejiang University College of Computer Science and Technology Hangzhou China Alibaba Group Hangzhou China

ISBN: (纸本)9781728163277

Multi-label zero-shot learning (ML-ZSL), with the difficulty of both multi-label learning and zero-shot learning, aims to recognize various unseen objects that are not observed during training. Previous methods mainly use a single directional visual-semantic mapping to associate the visual and semantic embedding space, which is not sufficient to adequately realize knowledge transfer from seen to unseen classes. In this paper, we propose a novel dual collaborative visual-semantic mapping framework, constructing abundant connection relationships by exploring two aspects of mapping streams, i.e., the visual-to-semantic (V2S) mapping and the semantic-to-visual (S2V) mapping. Through the collaborative learning of these two effective mappings, our method achieves state-of-the-art performance on the MS-COCO and PASCAL-VOC, two benchmarks for ML-ZSL. © 2023 ieee.

关键词： multi-label image recognition visual-semantic zero-shot learning

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A Stealthy Backdoor Attack Method Based on Generative Adversarial Networks 5

A Stealthy Backdoor Attack Method Based on Generative Advers...

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5th Asia-Pacific conference on image processing. Electronics and computers, IPEC 2024

作者： Tan, Yayue Yu, Gao Zhang, Yaqi Tang, Yongqing Southwest University College of Computer and Information Science School of Software Chongqing China Payment core platform Ant Group Co. Ltd Department of Payment Platform Technology Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350374407

Deep neural networks have been widely and successfully applied to many critical tasks, such as image pattern recognition, natural language processing.and autonomous driving, and large amounts of training data and increasing computational power are key factors in their success. Many studies in recent years have shown that deep neural network models face many practical security threats, such as adversarial attacks and backdoor attacks, which can have very serious consequences in the real world. Aiming at the problems of stealth, backdoor trigger design and universality of current backdoor attack methods, a novel method of invisible backdoor attack based on generative adversarial network is proposed. As a backdoor attack method based on data poisoning, inspired by image steganography, this method trains an encoder to generate poisoned samples through generative adversarial network to implement backdoor attack. The secret image used to guide the generation of backdoor triggers does not need to be carefully designed, and the encoder has good generalization ability across datasets. These features not only make its attack effective and stealthy, but also greatly reduce the cost of backdoor. Extensive experimental and analytical results show that the proposed backdoor attack method is effective, stealthy, sustainable and universal. © 2024 ieee.

关键词： Generative adversarial networks

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 ieee.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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One-Shot Medical Action recognition With A Cross-Attention Mechanism And Dynamic Time Warping 48

One-Shot Medical Action Recognition With A Cross-Attention M...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Xie, Leiyu Yang, Yuxing Fu, Zeyu Naqvi, Syed Mohsen Newcastle University Intelligent Sensing and Communications Research Group United Kingdom University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

In this paper, we address the classification of medical actions with only one single sample by developing a novel one-shot learning framework which contains both cross-attention and dynamic time warping (DTW) modules. To be concrete, we firstly transform the raw skeleton sequence into the signal-level image representation. We exploit a metric learning approach, which is the prototypical network for the proposed one-shot learning framework and choose the residual network (ResNet18) as the backbone which is widely used in recent years. Cross-attention is applied for guiding the network to focus on the more important joints from each specific action. The cross-attention mechanism that applies between the support and query set will be adapted for mining and matching the relationships with the human body. Furthermore, a DTW module is introduced to mitigate the temporal information mismatching issue between the actions from the support and query sets. The experimental results on the NTU RGB+D 120 dataset demonstrate the effectiveness of our proposed approach and the improved performance compared to the baseline approach. The code of this work is available at 1 © 2023 ieee.

关键词： image representation

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A Method for Soybean Germination Rate Detection Based on image processing 49

A Method for Soybean Germination Rate Detection Based on Ima...

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49th Annual conference of the ieee Industrial Electronics society, IECON 2023

作者： Han, Ru Shu, Lei Yin, Menghan Deng, Der-Jiunn Li, Kailiang Zhang, Jin Yi, Xuefang School of Artificial Intelligence Nanjing Agricultural University China Nanjing Agricultural University NAU-Lincoln Joint Research Center of Intelligent Engineering China School of Engineering University of Lincoln United Kingdom National Changhua University of Education Department of Computer Science and Information Engineering Taiwan China Telecom Hongxin Infomation Technology Company Nanjing China

ISBN: (纸本)9798350331820

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in *** solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing.techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate;(2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds. © 2023 ieee.

关键词： Germination

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Human Motion pattern Perception Algorithm Based on Heterogeneous Multiprocessor and Deep Learning

Human Motion Pattern Perception Algorithm Based on Heterogen...

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2023 Asia-Europe conference on Electronics, Data processing.and Informatics, ACEDPI 2023

作者： Qiu, Sanjun Police Physical Education Guangxi Police College Guangxi Nanning530029 China

ISBN: (纸本)9798350301151

With the continuous progress of mankind, the level of sports is particularly important in today's society. Mobile communication system is one of the key parts. This paper proposes a method of human biological signal processing.based on heterogeneous multiprocessor and deep learning. The algorithm utilizes the characteristics of real-time, scalability and high reliability, and can adapt to the needs of users with good performance, easy to use, and can realize human-computer interactive perception. After that, this paper describes the concept of heterogeneous multiprocessor and studies the application of deep learning algorithm. In this paper, the MATLAB programming language is used to verify its effectiveness, and the simulation results prove the accuracy of this scheme. The experimental results show that the correct rate of recognition features is basically above 90%. © 2023 ieee.

关键词： Mobile telecommunication systems

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Contour and key point based eye feature extraction

Contour and key point based eye feature extraction

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Wang, Yue Wan, Yaping Min, Xiaoshan Fan, Zhiqing He, Yasha Zou, Gang School of Computer Science University of South China Hengyang421001 China Hunan Medical Big Data International Science and Technology Cooperation Base Hengyang421001 China Xiangya School of Medicine Central South University Changsha410205 China School of Computer Science and Technology North University of China Taiyuan030000 China Hunan ZhongkeZhuying Intelligent Technology Research Institute Changsha410205 China

ISBN: (纸本)9781510666351

The use of eye movements for identifying intrinsic human psychological changes is a hot topic, and the identification of eye states in specific scenarios is the basis for analyzing the test taker's instantaneous psychological response state and thus for testing implicit driving quality deficits. Infrared eye images are difficult to visualize eye movements, and traditional eye image processing.results are not suitable for eye state analysis. In order to make the eye images show the eye movements, a feature extraction method based on contours and key point is proposed for infrared eye images. To extract the eye contour features, the iris and eyelid contours are extracted based on the variation of gray values between the points located on the boundary and their surroundings;for the extraction of eye key point features, a network design with a combination of dlib and residual structure is used to extract the pupil and eyelid key points, respectively. The experimental results show that the method can represent the relative positions of the pupil, iris and eyelid separately with little computational effort, which can intuitively show the eye movement and thus reduce the stress of subsequent eye state recognition. © 2023 SPIE.

关键词： Eye movements

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No-reference image quality assessment based on deep learning 3

No-reference image quality assessment based on deep learning

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3rd International conference on Advanced Algorithms and Signal image processing. AASIP 2023

作者： Wang, Yifan Zhang, Fan Chang, Sheng Zhang, Xinhong School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

images form the basis of human vision and are an important source of information for both human perception and machine pattern recognition. Since the development of the image quality evaluation field, a large number of image quality evaluation algorithms have emerged. In recent years, deep learning has become a hot area and has also been applied to the image domain. This paper focuses on reference-free image quality evaluation and reviews the no-reference image quality assessment based on deep learning. Firstly, the classification of IQA, technical indexes for IQA algorithm evaluation, and several public IQA databases available online are introduced. Then, several deep learning models applied to NR-IQA are discussed, compared, and evaluated in detail. Finally, an outlook on future research is provided. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Classification (of information)

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Compact and Effective Representations for Sketch-based image Retrieval

Compact and Effective Representations for Sketch-based Image...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA

ISBN: (纸本)9781665448994

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

关键词： Dimensionality reduction computer vision conferences image retrieval Memory management Search engines pattern recognition

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Application of artificial intelligence image recognition technology in power system 3

Application of artificial intelligence image recognition tec...

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3rd International conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Du, Xiaoyue Gu, Guangheng Yang, Fan Yunnan Power Grid Materials Co. Ltd. Kunming650220 China

ISBN: (纸本)9781510657281

Under the vigorous promotion of global energy Internet, China's AC/DC power grid construction has been developed rapidly and both artificial intelligence technology and machine learning technology have been developed by leaps and bounds. computer-based image pattern recognition technology is an important part of computer application technology. With the rapid development of digital image processing.technology and pattern recognition technology, image pattern recognition technology has been more comprehensively applied in various aspects such as industry, agriculture, national defense, scientific research and medicine. In this paper, we introduce the image detection technology and image detection algorithm for substation inspection robots and acquire image data files through image acquisition terminal sensors to realize the categorization of feature amount. So as to realize the annotation of pre-set features based on the training model. An optimized classifier and optimized image features are used to build an efficient and accurate image classification model. Artificial intelligence image recognition technology for power equipment image recognition is important to further improve the automation and intelligence level of substations. © 2022 SPIE.

关键词： image recognition

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