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Abstract ：
由于细粒度图像之间存在小的类间方差和大的类内差异,现有分类算法仅仅聚焦于单张图像的显著局部特征的提取与表示学习,忽视了多张图像之间局部的异构语义判别信息,较难关注到区分不同类别的微小细节,导致学习到的特征缺乏足够区分度.本文提出了一种渐进式网络以弱监督的方式学习图像不同粒度层级的信息.首先,构建一个注意力累计目标定位模块(Attention accumulation object localization module, AAOLM),在单张图像上从不同的训练轮次和特征提取阶段对注意力信息进行语义目标集成定位.其次,设计一个多张图像异构局部交互图模块(Heterogeneous local interactive graph module, HLIGM),提取每张图像的显著性局部区域特征,在类别标签引导下构建多张图像的局部区域特征之间的图网络,聚合局部特征增强表示的判别力.最后,利用知识蒸馏将异构局部交互图模块产生的优化信息反馈给主干网络,从而能够直接提取具有较强区分度的特征,避免了在测试阶段建图的计算开销.通过在多个数据集上进行的实验,证明了提出方法的有效性,能够提高细粒度分类的精度.
Keyword ：
图神经网络 图神经网络 弱监督目标定位 弱监督目标定位 深度学习 深度学习 知识蒸馏 知识蒸馏 细粒度图像分类 细粒度图像分类
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| GB/T 7714 | 陈权 , 陈飞 , 王衍根 et al. 融合目标定位与异构局部交互学习的细粒度图像分类 [J]. | 自动化学报 , 2024 , 50 (11) : 2219-2230 . |
| MLA | 陈权 et al. "融合目标定位与异构局部交互学习的细粒度图像分类" . | 自动化学报 50 . 11 (2024) : 2219-2230 . |
| APA | 陈权 , 陈飞 , 王衍根 , 程航 , 王美清 . 融合目标定位与异构局部交互学习的细粒度图像分类 . | 自动化学报 , 2024 , 50 (11) , 2219-2230 . |
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高强度的高斯噪声往往会模糊或破坏图像的细节和结构,导致边缘信息的丢失.为此,提出基于逐像素强化学习的边缘保持图像复原算法.首先,为每个像素构建一个像素层智能体并设计针对边缘处的侧窗均值滤波器到动作空间中,所有的像素层智能体共享优势行动者-评论家算法的参数,因此模型可以同时输出所有位置的状态转移概率并选择合适的策略进行状态转移,从而复原图像;其次,在特征提取共享网络中结合协调注意力,聚焦所有像素位置在特征通道间的全局信息,并保留位置嵌入信息;然后,为了缓解稀疏奖励问题,设计一个基于图拉普拉斯正则的辅助损失,关注图像的局部平滑信息,对局部不平滑区域加以惩罚,从而促进像素层智能体更加有效地学习到正确的策略以实现边缘保持.实验结果表明,所提的算法在Middlebury2005数据集和MNIST数据集上的峰值信噪比(PSNR)分别达到32.97 dB和28.26 dB,相比于Pixel-RL算法分别提升了 0.23 dB和0.75 dB,参数量和训练总时间分别减少了 44.9％和18.2％,在实现边缘保持的同时有效降低了模型的复杂度.
Keyword ：
协调注意力 协调注意力 图像复原 图像复原 图拉普拉斯 图拉普拉斯 深度强化学习 深度强化学习 边缘保持 边缘保持 逐像素强化学习 逐像素强化学习
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| GB/T 7714 | 江敏 , 陈飞 , 程航 et al. 基于逐像素强化学习的边缘保持图像复原 [J]. | 计算机工程 , 2024 , 50 (12) : 224-232 . |
| MLA | 江敏 et al. "基于逐像素强化学习的边缘保持图像复原" . | 计算机工程 50 . 12 (2024) : 224-232 . |
| APA | 江敏 , 陈飞 , 程航 , 王美清 . 基于逐像素强化学习的边缘保持图像复原 . | 计算机工程 , 2024 , 50 (12) , 224-232 . |
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In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respect to an underlying graph. However, for signals slowly varying across graph kernels, GLR suffers from an undesirable "staircase" effect. In this paper, focusing on manifold graphs-collections of uniform discrete samples on low-dimensional continuous manifolds-we generalize GLR to gradient graph Laplacian regularizer (GGLR) that promotes planar / piecewise planar (PWP) signal reconstruction. Specifically, for a graph endowed with sampling coordinates (e.g., 2D images, 3D point clouds), we first define a gradient operator, using which we construct a gradient graph for nodes' gradients in the sampling manifold space. This maps to a gradient-induced nodal graph (GNG) and a positive semi-definite (PSD) Laplacian matrix with planar signals as the 0 frequencies. For manifold graphs without explicit sampling coordinates, we propose a graph embedding method to obtain node coordinates via fast eigenvector computation. We derive the means-square-error minimizing weight parameter for GGLR efficiently, trading off bias and variance of the signal estimate. Experimental results show that GGLR outperformed previous graph signal priors like GLR and graph total variation (GTV) in a range of graph signal restoration tasks.
Keyword ：
graph embedding graph embedding Graph signal processing Graph signal processing graph smoothness priors graph smoothness priors quadratic programming quadratic programming
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| GB/T 7714 | Chen, Fei , Cheung, Gene , Zhang, Xue . Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer [J]. | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2024 , 72 : 744-761 . |
| MLA | Chen, Fei et al. "Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer" . | IEEE TRANSACTIONS ON SIGNAL PROCESSING 72 (2024) : 744-761 . |
| APA | Chen, Fei , Cheung, Gene , Zhang, Xue . Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer . | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2024 , 72 , 744-761 . |
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Background: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. Purpose: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. Materials and methods: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. Results: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). Conclusion: The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.
Keyword ：
artificial intelligence artificial intelligence computer vision model computer vision model deep learning deep learning endoscopic thyroid surgery endoscopic thyroid surgery ischemia alert ischemia alert parathyroid gland recognition parathyroid gland recognition
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| GB/T 7714 | Wang, Bo , Yu, Jia-Fan , Lin, Si-Ying et al. Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery [J]. | HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK , 2024 , 46 (8) : 1975-1987 . |
| MLA | Wang, Bo et al. "Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery" . | HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK 46 . 8 (2024) : 1975-1987 . |
| APA | Wang, Bo , Yu, Jia-Fan , Lin, Si-Ying , Li, Yi-Jian , Huang, Wen-Yu , Yan, Shou-Yi et al. Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery . | HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK , 2024 , 46 (8) , 1975-1987 . |
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Image steganography attempts to imperceptibly hide the secret image within the cover image. Most of the existing deep learning -based steganography approaches have excelled in payload capacity, visual quality, and steganographic security. However, they are difficult to losslessly reconstruct secret images from stego images with relatively large payload capacity. Recently, although some studies have introduced invertible neural networks (INNs) to achieve largecapacity image steganography, these methods still cannot reconstruct the secret image losslessly due to the existence of lost information on the output side of the concealing network. We present an INN -based framework in this paper for lossless image steganography. Specifically, we regard image steganography as an image super -resolution task that converts low -resolution cover images to high -resolution stego images while hiding secret images. The feature dimension of the generated stego image matches the total dimension of the input secret and cover images, thereby eliminating the lost information. Besides, a bijective secret projection module is designed to transform various secret images into a latent variable that follows a simple distribution, improving the imperceptibility of the secret image. Comprehensive experiments indicate that the proposed framework achieves secure hiding and lossless extraction of the secret image.
Keyword ：
Covert communication Covert communication Information security Information security Invertible neural networks Invertible neural networks Lossless steganography Lossless steganography
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| GB/T 7714 | Wang, Tingqiang , Cheng, Hang , Liu, Ximeng et al. Lossless image steganography: Regard steganography as super-resolution [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (4) . |
| MLA | Wang, Tingqiang et al. "Lossless image steganography: Regard steganography as super-resolution" . | INFORMATION PROCESSING & MANAGEMENT 61 . 4 (2024) . |
| APA | Wang, Tingqiang , Cheng, Hang , Liu, Ximeng , Xu, Yongliang , Chen, Fei , Wang, Meiqing et al. Lossless image steganography: Regard steganography as super-resolution . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (4) . |
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由于细粒度图像类间差异小,类内差异大的特点,因此细粒度图像分类任务关键在于寻找类别间细微差异.最近,基于Vision Transformer的网络大多侧重挖掘图像最显著判别区域特征.这存在两个问题:首先,网络忽略从其他判别区域挖掘分类线索,容易混淆相似类别;其次,忽略了图像的结构关系,导致提取的类别特征不准确.为解决上述问题,本文提出动态自适应调制和结构关系学习两个模块,通过动态自适应调制模块迫使网络寻找多个判别区域,再利用结构关系学习模块构建判别区域间结构关系;最后利用图卷积网络融合语义信息和结构信息得出预测分类结果.所提出的方法在CUB-200-2011数据集和NA-Birds数据集上测试准确率分别达到92.9%和93.0%,优于现有最先进网络.
Keyword ：
Vision Transformer (ViT) Vision Transformer (ViT) 动态自适应调制 动态自适应调制 图卷积网络 图卷积网络 细粒度图像分类 细粒度图像分类 结构关系学习 结构关系学习
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| GB/T 7714 | 王衍根 , 陈飞 , 陈权 . 结合动态自适应调制和结构关系学习的细粒度图像分类 [J]. | 计算机系统应用 , 2024 , 33 (08) : 166-175 . |
| MLA | 王衍根 et al. "结合动态自适应调制和结构关系学习的细粒度图像分类" . | 计算机系统应用 33 . 08 (2024) : 166-175 . |
| APA | 王衍根 , 陈飞 , 陈权 . 结合动态自适应调制和结构关系学习的细粒度图像分类 . | 计算机系统应用 , 2024 , 33 (08) , 166-175 . |
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针对计算机科学与技术专业实践教学改革，提出融合知识传授、能力培养和价值塑造的多元协同教学体系，以语音识别系统综合实践教学的开展为例，说明如何构建线上线下融通互补与校企协同的育人模式。设计多维度数据分析的过程性评价体系，以落实PBL育人理念，将价值塑造和创新思维培养贯穿新工科实践教学全过程。
Keyword ：
PBL PBL 人工智能技术 人工智能技术 实践教学 实践教学 新工科 新工科 语音识别 语音识别
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| GB/T 7714 | 刘莞玲 , 叶福玲 , 陈飞 . 新工科背景下的系统综合实践教学改革与探索 [J]. | 计算机教育 , 2024 , PageCount-页数: 5 (07) : 197-201 . |
| MLA | 刘莞玲 et al. "新工科背景下的系统综合实践教学改革与探索" . | 计算机教育 PageCount-页数: 5 . 07 (2024) : 197-201 . |
| APA | 刘莞玲 , 叶福玲 , 陈飞 . 新工科背景下的系统综合实践教学改革与探索 . | 计算机教育 , 2024 , PageCount-页数: 5 (07) , 197-201 . |
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The health-related Internet of Things (IoT) plays an irreplaceable role in the collection, analysis, and transmission of medical data. As a device of the health-related IoT, the electroencephalogram (EEG) has long been a powerful tool for physiological and clinical brain research, which contains a wealth of personal information. Due to its rich computational/storage resources, cloud computing is a promising solution to extract the sophisticated feature of massive EEG signals in the age of big data. However, it needs to solve both response latency and privacy leakage. To reduce latency between users and servers while ensuring data privacy, we propose a privacy-preserving feature extraction scheme, called LightPyFE, for EEG signals in the edge computing environment. In this scheme, we design an outsourced computing toolkit, which allows the users to achieve a series of secure integer and floating-point computing operations. During the implementation, LightPyFE can ensure that the users just perform the encryption and decryption operations, where all computing tasks are outsourced to edge servers for specific processing. Theoretical analysis and experimental results have demonstrated that our scheme can successfully achieve privacy-preserving feature extraction for EEG signals, and is practical yet effective.
Keyword ：
Additive secret sharing Additive secret sharing edge computing edge computing electroencephalogram (EEG) signal electroencephalogram (EEG) signal Internet of Things (IoT) Internet of Things (IoT) privacy-preserving privacy-preserving
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| GB/T 7714 | Yan, Nazhao , Cheng, Hang , Liu, Ximeng et al. Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) : 2520-2533 . |
| MLA | Yan, Nazhao et al. "Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing" . | IEEE INTERNET OF THINGS JOURNAL 11 . 2 (2024) : 2520-2533 . |
| APA | Yan, Nazhao , Cheng, Hang , Liu, Ximeng , Chen, Fei , Wang, Meiqing . Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) , 2520-2533 . |
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Image denoising is a fundamental tool in the fields of image processing and computer vision. With the rapid development of multimedia and cloud computing, it has become popular for resource-constrained users to outsource the storage and denoising of massive images. However, it may cause privacy concerns and response delays. In this scenario, we propose an efFicient privAcy-preseRving Image deNoising schEme (FARINE) for outsourcing digital images. By introducing a key conversion mechanism, FARINE allows removing noise from a given noisy image using a non-local mean way without leaking any information about the plaintext content. Due to its low computational latency/communication cost, edge computing is considered to improve the user experience. To achieve a dynamic user set efficiently, we design a fine-grained access control mechanism to support user authorization and revocation in multi-user scenarios. Extensive experiments over several benchmark data sets show that FARINE obtains comparable performance to plaintext image denoising.
Keyword ：
access control access control edge computing edge computing homomorphic encryption homomorphic encryption image denoising image denoising Privacy-preserving Privacy-preserving
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| GB/T 7714 | Huang, Yibing , Xu, Yongliang , Cheng, Hang et al. Edge-based secure image denoising scheme supporting flexible user authorization [J]. | JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY , 2024 , 18 . |
| MLA | Huang, Yibing et al. "Edge-based secure image denoising scheme supporting flexible user authorization" . | JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 18 (2024) . |
| APA | Huang, Yibing , Xu, Yongliang , Cheng, Hang , Chen, Fei , Wang, Meiqing . Edge-based secure image denoising scheme supporting flexible user authorization . | JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY , 2024 , 18 . |
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Existing vision-language pre-training models typically extract region features and conduct fine-grained local alignment based on masked image/text completion or object detection methods. However, these models often design independent subtasks for different modalities, which may not adequately leverage interactions between modalities, requiring large datasets to achieve optimal performance. To address these limitations, this paper introduces a novel pre-training approach that facilitates fine-grained vision-language interaction. We propose two new subtasks — image filling and text filling — that utilize data from one modality to complete missing parts in another, enhancing the model's ability to integrate multi-modal information. A selector mechanism is also developed to minimize semantic overlap between modalities, thereby improving the efficiency and effectiveness of the pre-trained model. Our comprehensive experimental results demonstrate that our approach not only fosters better semantic associations among different modalities but also achieves state-of-the-art performance on downstream vision-language tasks with significantly smaller datasets. © 2024 Elsevier Ltd
Keyword ：
Cross-modal Cross-modal Image captioning Image captioning Partial auxiliary Partial auxiliary Pre-training Pre-training
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| GB/T 7714 | Cheng, H. , Ye, H. , Zhou, X. et al. Vision-language pre-training via modal interaction [J]. | Pattern Recognition , 2024 , 156 . |
| MLA | Cheng, H. et al. "Vision-language pre-training via modal interaction" . | Pattern Recognition 156 (2024) . |
| APA | Cheng, H. , Ye, H. , Zhou, X. , Liu, X. , Chen, F. , Wang, M. . Vision-language pre-training via modal interaction . | Pattern Recognition , 2024 , 156 . |
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