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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI United States Department of Computer Vision Baidu Inc. China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based Vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2024 Neural information processing systems foundation. All rights reserved.

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A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

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Deep Graph Convolutional Neural Network based on Adaptive Sampling and Aggregation Strategy 2

Deep Graph Convolutional Neural Network based on Adaptive Sa...

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2nd International Conference on Machine Vision, Image processing and Imaging technology, MVIPIT 2024

作者： Wang, Xueli Zhang, Wei Li, Gege Zhou, Lin Liu, Hongkai Ye, Zhonglin School of Computer Qinghai Normal University Xining810008 China Qinghai Provincial Key Laboratory of Tibetan Information Processing and Machine Translation Xining810008 China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki851-0193 Japan

ISBN: (纸本)9798331543037

Graph neural networks learn node embeddings by recursively sampling and aggregating nodes in a graph, while existing methods have a fixed pattern of node sampling and aggregation, and usually only consider direct neighborhood. When sampling high-order neighborhood, it is easy to produce over-smoothing, which affects the model performance and accuracy. Therefore, this paper proposes an adaptive sampling and aggregation deep graph convolutional network (Adaptive Sampling and Aggregation Depth Graph Convolutional Network, ASA-GCN). A sampling strategy based on node importance is designed to adaptively sample high-order neighbors of nodes. Meanwhile, an aggregation strategy based on self-attention mechanism is proposed to adaptively aggregate node information within a given neighborhood. Finally, experimental results on multiple benchmark graph datasets show that the model has superior performance on the node classification task. ©2024 IEEE.

关键词： Convolutional neural networks

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Research and Application of Speech Transmission Index in Acoustic Leakage Detection 17

Research and Application of Speech Transmission Index in Aco...

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17th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Hao, Qiuyun Wang, FuQiang Zhang, Peng Ma, XiaoFeng Zhang, Jianqiang Zhao, Wei Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331507398

Acoustic information in confidential premise can be leaked through channels such as walls, pipes, doors and windows. Acoustic leakage level needs to be measured through specific indicators. Acoustic leakage evaluation indicator includes subjective evaluation indicator and objective evaluation indicator. The subjective evaluation indicator is a qualitative evaluation of acoustic leakage through subjective auditory perception of human ear, which can directly reflect the degree of acoustic leakage in confidential premise. At present, the objective evaluation indicator is mainly sound transmission loss (STL) to reflect objective degree of acoustic leakage in confidential premise. Therefore, it is relatively singular. Through study of Speech Transmission Index (STI), speech transmission index for public address systems (STIPA), which is a simplified form of FULL STI, is applied to acoustic leakage detection in this paper. STI has been proposed as an objective evaluation indicator and applied to acoustic leakage detection and acoustic information security. In order to confirm the effectiveness of objective evaluation indicator, a large number of related experimental studies have been conducted in the paper. Through experiments, it has been proven that STI can objectively evaluate level of acoustic leakage and level of acoustic information security in confidential premise. STI can better predict level of Chinese clarity under various complex transmission conditions. It provides a reliable basis for speech privacy of confidential premise and evaluation of architectural acoustic, sound reinforcement system and voice communication system. © 2024 IEEE.

关键词： information leakage

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LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding 6

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Yu Wu, Xiaoming Yuan, Qile Dong, Yunfeng Liu, Xiangzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798331541699

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards. © 2024 IEEE.

关键词： Blockchain

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Multi-scale and attention training of uncalibrated photometric stereo networks

Multi-scale and attention training of uncalibrated photometr...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Zheng, Tianhang Cao, Lili Zhu, Yongjian School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou China Zhejiang Provincial Key Laboratory of Food Logistics Equipment and Technology Zhejiang University of Science and Technology Hangzhou China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9781510677685

In this paper, an uncalibrated photometric stereo network model is proposed. Traditional photometric stereo has precise requirements on light source and object surface reflectance, which greatly limits the usability of photometric stereo, and the ideal network model should be able to handle arbitrary objects under uncalibrated light sources to complete the reconstruction. To solve this problem, we propose an uncalibrated photometric stereo neural network, First, an arbitrary number of images are input to the first-stage neural network system to estimate the light source information and complete the calibration to eliminate the dependence on the light source information. Then the calibration result is input to the second-stage neural network together with the image, and the multi-scale and attention mechanism is used to obtain the object surface texture information to achieve the surface normal reconstruction of the object. Our network (MAPS-Net) is compared with other uncalibrated photometric stereo methods on the DiLiGenT benchmark, and the average MAE achieves an excellent performance of 9.20. © 2024 SPIE.

关键词： Deep neural networks

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Sample Reduction Using 1 -Norm Twin Bounded Support Vector Machine 2nd

Sample Reduction Using 1 -Norm Twin Bounded Support Vector M...

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Zheng, Xiaohan Zhang, Li Yan, Leilei School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9789811651878

Twin support machine (TSVM) has a lower time complexity than support vector machine (SVM), but it has a poor ability to perform sample reduction. In order to improve the ability of TSVM to reduce sample, we propose an 1 -norm twin bounded support machine (1 -TBSVM) inspired by the sparsity of 1 -norm in feature space. The objective function of 1 -TBSVM contains the hinge loss and the 1 -norm terms, both which can induce sparsity. We solve the primal programming problems of 1 -TBSVM to prevent the disappearance of sparsity and avoid the situation that the inverse of matrix does not exist. Thus, 1 -TBSVM has a good sparsity, or a good ability to reduce sample. Experimental results on synthetic and UCI datasets indicate that 1 -TBSVM has a good ability to perform sample reduction and simultaneously enhances the classification performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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HybridHash: Hybrid Convolutional and Self-Attention Deep Hashing for Image Retrieval

arXiv

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arXiv 2024年

作者： He, Chao Wei, Hongxi School of Computer Science Inner Mongolia University Provincial Key Laboratory of Mongolian Information Processing Technology National and Local Joint Engineering Research Center of Mongolian Information Processing Hohhot China

Deep image hashing aims to map input images into simple binary hash codes via deep neural networks and thus enable effective large-scale image retrieval. Recently, hybrid networks that combine convolution and Transformer have achieved superior performance on various computer tasks and have attracted extensive attention from researchers. Nevertheless, the potential benefits of such hybrid networks in image retrieval still need to be verified. To this end, we propose a hybrid convolutional and self-attention deep hashing method known as HybridHash. Specifically, we propose a backbone network with stage-wise architecture in which the block aggregation function is introduced to achieve the effect of local self-attention and reduce the computational complexity. The interaction module has been elaborately designed to promote the communication of information between image blocks and to enhance the visual representations. We have conducted comprehensive experiments on three widely used datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the method proposed in this paper has superior performance with respect to state-of-the-art deep hashing methods. Source code is available https://***/shuaichaochao/HybridHash. Copyright © 2024, The Authors. All rights reserved.

关键词： Image retrieval

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Leveraging Hybrid Referring Expressions for Referring Video Object Segmentation

Leveraging Hybrid Referring Expressions for Referring Video ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yan Li Qiong Wang College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Existing referring video object segmentation methods segment the target object referred by one referring expression or a simple group of multiple referring expressions by word concatenation in all video frames, which might still suffer from the issue of wrong object identification. To solve this problem, we propose a new transformer-based framework built upon hybrid referring expressions. To fully explore the multi-model fusion, three interaction methods are studied, and the early interaction shows the best alignment between discriminative video features and multi-language semantics. Moreover, two ways are explored to generate conditional queries in transformer-based methods, and the proposed word-group level method can better represent the target instance. Experiments on the large-scale Ref-YoutubeVOS benchmark show that the proposed model achieves 63.8% J & F, exceeding the state-of-the-art methods.

关键词： Semantics Object segmentation Benchmark testing Streaming media Transformers Object recognition

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