Image and Vision Computing最新文献_第5页

PTPFusion: A progressive infrared and visible image fusion network based on texture preserving PTPFusion：基于纹理保护的渐进式红外与可见光图像融合网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-25 DOI: 10.1016/j.imavis.2024.105287

Yixiang Lu , Weijian Zhang , Dawei Zhao , Yucheng Qian , Davydau Maksim , Qingwei Gao

{"title":"PTPFusion: A progressive infrared and visible image fusion network based on texture preserving","authors":"Yixiang Lu , Weijian Zhang , Dawei Zhao , Yucheng Qian , Davydau Maksim , Qingwei Gao","doi":"10.1016/j.imavis.2024.105287","DOIUrl":"10.1016/j.imavis.2024.105287","url":null,"abstract":"<div><div>Infrared and visible image fusion aims to provide a more comprehensive image for downstream tasks by highlighting the main target and maintaining rich texture information. Image fusion methods based on deep learning suffer from insufficient multimodal information extraction and texture loss. In this paper, we propose a texture-preserving progressive fusion network (PTPFusion) to extract complementary information from multimodal images to solve these issues. To reduce image texture loss, we design multiple consecutive texture-preserving blocks (TPB) to enhance fused texture. The TPB can enhance the features by using a parallel architecture consisting of a residual block and derivative operators. In addition, a novel cross-channel attention (CCA) fusion module is developed to obtain complementary information by modeling global feature interactions via cross-queries mechanism, followed by information fusion to highlight the feature of the salient target. To avoid information loss, the extracted features at different stages are merged as the output of TPB. Finally, the fused image will be generated by the decoder. Extensive experiments on three datasets show that our proposed fusion algorithm is better than existing state-of-the-art methods.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105287"},"PeriodicalIF":4.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dictionary learning based unsupervised neural network for single image compressed sensing 基于字典学习的单图像压缩传感无监督神经网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-23 DOI: 10.1016/j.imavis.2024.105281

Kuang Luo , Lu Ou , Ming Zhang , Shaolin Liao , Chuangfeng Zhang

{"title":"A dictionary learning based unsupervised neural network for single image compressed sensing","authors":"Kuang Luo , Lu Ou , Ming Zhang , Shaolin Liao , Chuangfeng Zhang","doi":"10.1016/j.imavis.2024.105281","DOIUrl":"10.1016/j.imavis.2024.105281","url":null,"abstract":"<div><div>In the field of Compressed Sensing (CS), the sparse representation of signals and the advancement of reconstruction algorithms are two critical challenges. However, conventional CS algorithms often fail to sufficiently exploit the structured sparsity present in images and suffer from poor reconstruction quality. Most deep learning-based CS methods are typically trained on large-scale datasets. Obtaining a sufficient number of training sets is challenging in many practical applications and there may be no training sets available at all in some cases. In this paper, a novel deep Dictionary Learning (DL) based unsupervised neural network for single image CS (dubbed DL-CSNet) is proposed. It is an effective trainless neural network that consists of three components and their corresponding loss functions: 1) a DL layer that consists of multi-layer perceptron (MLP) and convolution neural networks (CNN) for latent sparse features extraction with the L1-norm sparsity loss function; 2) an image smoothing layer with the Total Variation (TV) like image smoothing loss function; and 3) a CS acquisition layer for image compression, with the Mean Square Error (MSE) loss function between the original image compression and the reconstructed image compression. In particular, the proposed DL-CSNet is a lightweight and fast model that does not require datasets for training and exhibits a fast convergence speed, making it suitable for deployment in resource-constrained environments. Experiments have demonstrated that the proposed DL-CSNet achieves superior performance compared to traditional CS methods and other unsupervised state-of-the-art deep learning-based CS methods.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105281"},"PeriodicalIF":4.2,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unbiased scene graph generation via head-tail cooperative network with self-supervised learning 通过具有自我监督学习功能的头尾协同网络生成无偏差场景图

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-22 DOI: 10.1016/j.imavis.2024.105283

Lei Wang , Zejian Yuan , Yao Lu , Badong Chen

{"title":"Unbiased scene graph generation via head-tail cooperative network with self-supervised learning","authors":"Lei Wang , Zejian Yuan , Yao Lu , Badong Chen","doi":"10.1016/j.imavis.2024.105283","DOIUrl":"10.1016/j.imavis.2024.105283","url":null,"abstract":"<div><div>Scene Graph Generation (SGG) as a critical task in image understanding, facing the challenge of head-biased prediction caused by the long-tail distribution of predicates. However, current debiased SGG methods can easily prioritize improving the prediction of tail predicates while ignoring the substantial sacrifice of head predicates, leading to a shift from head bias to tail bias. To address this issue, we propose a Head-Tail Cooperative network with self-supervised Learning (HTCL), which achieves unbiased SGG by cooperating head-prefer and tail-prefer predictions through learnable weight parameters. HTCL employs a tail-prefer feature encoder to re-represent predicate features by injecting self-supervised learning, which focuses on the intrinsic structure of features, into the supervised learning of SGG, constraining the representation of predicate features to enhance the distinguishability of tail samples. We demonstrate the effectiveness of our HTCL by applying it to VG150, Open Images V6 and GQA200 datasets. The results show that HTCL achieves higher mean Recall with a minimal sacrifice in Recall and achieves a new state-of-the-art overall performance. Our code is available at <span><span>https://github.com/wanglei0618/HTCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105283"},"PeriodicalIF":4.2,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UIR-ES: An unsupervised underwater image restoration framework with equivariance and stein unbiased risk estimator UIR-ES：使用等差数列和斯坦因无偏风险估计器的无监督水下图像修复框架

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-22 DOI: 10.1016/j.imavis.2024.105285

Jiacheng Zhu , Junjie Wen , Duanqin Hong , Zhanpeng Lin , Wenxing Hong

{"title":"UIR-ES: An unsupervised underwater image restoration framework with equivariance and stein unbiased risk estimator","authors":"Jiacheng Zhu , Junjie Wen , Duanqin Hong , Zhanpeng Lin , Wenxing Hong","doi":"10.1016/j.imavis.2024.105285","DOIUrl":"10.1016/j.imavis.2024.105285","url":null,"abstract":"<div><div>Underwater imaging faces challenges for enhancing object visibility and restoring true colors due to the absorptive and scattering characteristics of water. Underwater image restoration (UIR) seeks solutions to restore clean images from degraded ones, providing significant utility in downstream tasks. Recently, data-driven UIR has garnered much attention due to the potent expressive capabilities of deep neural networks (DNNs). These DNNs are supervised, relying on a large amount of labeled training samples. However, acquiring such data is expensive or even impossible in real-world underwater scenarios. While recent researches suggest that unsupervised learning is effective in UIR, none of these frameworks consider signal physical priors. In this work, we present a novel physics-inspired unsupervised UIR framework empowered by equivariance and unbiased estimation techniques. Specifically, equivariance stems from the invariance, inherent in natural signals to enhance data-efficient learning. Given that degraded images invariably contain noise, we propose a noise-tolerant loss for unsupervised UIR based on the Stein unbiased risk estimator to achieve an accurate estimation of the data consistency. Extensive experiments on the benchmark UIR datasets, including the UIEB and RUIE datasets, validate the superiority of the proposed method in terms of quantitative scores, visual outcomes, and generalization ability, compared to state-of-the-art counterparts. Moreover, our method demonstrates even comparable performance with the supervised model.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105285"},"PeriodicalIF":4.2,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regularization by denoising diffusion process meets deep relaxation in phase 去噪扩散过程的正则化与相位深度松弛的结合

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-20 DOI: 10.1016/j.imavis.2024.105282

Eunju Cha

{"title":"Regularization by denoising diffusion process meets deep relaxation in phase","authors":"Eunju Cha","doi":"10.1016/j.imavis.2024.105282","DOIUrl":"10.1016/j.imavis.2024.105282","url":null,"abstract":"<div><div>Fourier phase retrieval is one of the representative inverse problems where a signal needs to be recovered using only the measured magnitude of its Fourier transform. Deep learning-based algorithms for solving Fourier phase retrieval have been widely studied. These methods provide better reconstruction than the conventional algorithms, such as alternating projection approaches and convex relaxation methods. However, it is difficult to recover the phase information of 256 × 256 images accurately, and they often cannot provide fine details and textures. Recently, diffusion models have been used to solve Fourier phase retrieval problems. They offer realistic reconstruction results, but due to the nature of generative models, they often create non-existent features in the actual images. To address these issues, we introduced a novel algorithm inspired by regularization by denoising diffusion, a variational diffusion sampling for reconstructing the images from the measurements. In particular, the optimization problem in the convex relaxation approach for phase retrieval is interpreted as an additional constraint during the variational sampling process to estimate the phase from the given Fourier magnitude measurement. The proposed method stands out by leveraging not only pre-trained diffusion models as image priors but also the classical optimization approach as the regularization. This novel combination ensures not just accurate phase reconstruction, but also performance guarantees. Our experiments demonstrate that the proposed algorithm consistently provides state-of-the-art performance across various datasets of 256 × 256 images. We further showed the effectiveness of the new regularization for the performance gain in the phase estimation.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105282"},"PeriodicalIF":4.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new deepfake detection model for responding to perception attacks in embodied artificial intelligence 在具身人工智能中应对感知攻击的新型深度伪造检测模型

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-19 DOI: 10.1016/j.imavis.2024.105279

JunShuai Zheng , XiYuan Hu , Chen Chen , YiChao Zhou , DongYang Gao , ZhenMin Tang

{"title":"A new deepfake detection model for responding to perception attacks in embodied artificial intelligence","authors":"JunShuai Zheng , XiYuan Hu , Chen Chen , YiChao Zhou , DongYang Gao , ZhenMin Tang","doi":"10.1016/j.imavis.2024.105279","DOIUrl":"10.1016/j.imavis.2024.105279","url":null,"abstract":"<div><div>Embodied artificial intelligence (AI) represents a new generation of robotics technology combined with artificial intelligence, and it is at the forefront of current research. To reduce the impact of deepfake technology on embodied perception and enhance the security and reliability of embodied AI, this paper proposes a novel deepfake detection model with a new Balanced Contrastive Learning strategy, named BCL. By integrating unsupervised contrastive learning and supervised contrastive learning with deepfake detection, the model effectively extracts the underlying features of fake images from both individual level and category level, thereby leading to more discriminative features. In addition, a Multi-scale Attention Interaction module (MAI) is proposed to enrich the representative ability of features. By cross-fusing the semantic features of different receptive fields of the encoder, more effective deepfake traces can be mined. Finally, extensive experiments demonstrate that our method has good performance and generalization capabilities across intra-dataset, cross-dataset and cross-manipulation scenarios.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105279"},"PeriodicalIF":4.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ground4Act: Leveraging visual-language model for collaborative pushing and grasping in clutter Ground4Act：利用视觉语言模型，在杂乱无章的环境中协同推动和抓取

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-18 DOI: 10.1016/j.imavis.2024.105280

Yuxiang Yang , Jiangtao Guo , Zilong Li , Zhiwei He , Jing Zhang

{"title":"Ground4Act: Leveraging visual-language model for collaborative pushing and grasping in clutter","authors":"Yuxiang Yang , Jiangtao Guo , Zilong Li , Zhiwei He , Jing Zhang","doi":"10.1016/j.imavis.2024.105280","DOIUrl":"10.1016/j.imavis.2024.105280","url":null,"abstract":"<div><div>The challenge in robotics is to enable robots to transition from visual perception and language understanding to performing tasks such as grasp and assembling objects, bridging the gap between “seeing” and “hearing” to “doing”. In this work, we propose Ground4Act, a two-stage approach for collaborative pushing and grasping in clutter using a visual-language model. In the grounding stage, Ground4Act extracts target features from multi-modal data via visual grounding. In the action stage, it embeds a collaborative pushing and grasping framework to generate the action's position and direction. Specifically, we propose a DQN-based reinforcement learning pushing policy that uses RGBD images as the state space to determine the push action's pixel-level coordinates and direction. Additionally, a least squares-based linear fitting grasping policy takes the target mask from the grounding stage as input to achieve efficient grasp. Simulations and real-world experiments demonstrate Ground4Act's superior performance. The simulation suite, source code, and trained models will be made publicly available.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105280"},"PeriodicalIF":4.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0262885624003858/pdfft?md5=14502c48d797b01e4d54229138caf4f2&pid=1-s2.0-S0262885624003858-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A learnable motion preserving pooling for action recognition 用于动作识别的可学习运动保护池

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-17 DOI: 10.1016/j.imavis.2024.105278

Tankun Li , Kwok Leung Chan , Tardi Tjahjadi

{"title":"A learnable motion preserving pooling for action recognition","authors":"Tankun Li , Kwok Leung Chan , Tardi Tjahjadi","doi":"10.1016/j.imavis.2024.105278","DOIUrl":"10.1016/j.imavis.2024.105278","url":null,"abstract":"<div><div>Using deep neural networks (DNN) for video understanding tasks is expensive in terms of computation cost. Pooling layers in DNN which are widely used in most vision tasks to resize the spatial dimensions play crucial roles in reducing the computation and memory cost. In video-related tasks, pooling layers are also applied, mostly in the spatial dimension only as the standard average pooling in the temporal domain can significantly reduce its performance. This is because conventional temporal pooling degrades the underlying important motion features in consecutive frames. Such a phenomenon is rarely investigated and most state-of-art methods simply do not adopt temporal pooling, leading to enormous computation costs. In this work, we propose a learnable motion-preserving pooling (MPPool) layer that is able to preserve the general motion progression after the pooling. This pooling layer first locates the frames with the strongest motion features and then keeps these crucial features during pooling. Our experiments demonstrate that MPPool not only reduces the computation cost for video data modeling, but also increases the final prediction accuracy on various motion-centric and appearance-centric datasets.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105278"},"PeriodicalIF":4.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LVD-YOLO: An efficient lightweight vehicle detection model for intelligent transportation systems LVD-YOLO：用于智能交通系统的高效轻型车辆检测模型

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-16 DOI: 10.1016/j.imavis.2024.105276

Hao Pan, Shaopeng Guan, Xiaoyan Zhao

{"title":"LVD-YOLO: An efficient lightweight vehicle detection model for intelligent transportation systems","authors":"Hao Pan, Shaopeng Guan, Xiaoyan Zhao","doi":"10.1016/j.imavis.2024.105276","DOIUrl":"10.1016/j.imavis.2024.105276","url":null,"abstract":"<div><p>Vehicle detection is a fundamental component of intelligent transportation systems. However, current algorithms often encounter issues such as high computational complexity, long execution times, and significant resource demands, making them unsuitable for resource-limited environments. To overcome these challenges, we propose LVD-YOLO, a Lightweight Vehicle Detection Model based on YOLO. This model incorporates the EfficientNetv2 network structure as its backbone, which reduces parameters and enhances feature extraction capabilities. By utilizing a bidirectional feature pyramid structure along with a dual attention mechanism, we enable efficient information exchange across feature layers, thereby improving multiscale feature fusion. Additionally, we refine the model's loss function with SIoU loss to boost regression and prediction performance. Experimental results on the PASCAL VOC and MS COCO datasets show that LVD-YOLO outperforms YOLOv5s, achieving a 0.5% increase in accuracy while reducing FLOPs by 64.6% and parameters by 48.6%. These improvements highlight its effectiveness for use in resource-constrained environments.</p></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105276"},"PeriodicalIF":4.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CRFormer: A cross-region transformer for shadow removal CRFormer：用于去除阴影的跨区域变换器

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2024-09-16 DOI: 10.1016/j.imavis.2024.105273

Jin Wan , Hui Yin , Zhenyao Wu , Xinyi Wu , Zhihao Liu , Song Wang

{"title":"CRFormer: A cross-region transformer for shadow removal","authors":"Jin Wan , Hui Yin , Zhenyao Wu , Xinyi Wu , Zhihao Liu , Song Wang","doi":"10.1016/j.imavis.2024.105273","DOIUrl":"10.1016/j.imavis.2024.105273","url":null,"abstract":"<div><div>Image shadow removal is a fundamental task in computer vision, which aims to restore damaged signals caused by shadows, thereby improving image quality and scene understanding. Recently, transformers have demonstrated strong capabilities in various applications by capturing global pixel interactions, a capability highly desirable for shadow removal. However, applying transformers to promote shadow removal is non-trivial for the following two reasons: 1) The patchify operation is not suitable for shadow removal due to irregular shadow shapes; 2) Shadow removal only requires one-way interaction from the non-shadow region to the shadow region instead of the common two-way interactions among all pixels in the image. In this paper, we propose a novel <strong>C</strong>ross-<strong>R</strong>egion trans<strong>F</strong>ormer (CRFormer) for shadow removal which differs from existing transformers by only considering the pixel interactions from the non-shadow region to the shadow region without splitting images into patches. This is achieved by a carefully designed region-aware cross-attention mechanism that aggregates the recovered shadow region features, conditioned on the non-shadow region features. Extensive experiments on the ISTD, AISTD, SRD, and Video Shadow Removal datasets demonstrate the superiority of our method compared to other state-of-the-art methods.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105273"},"PeriodicalIF":4.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0