IEEE Transactions on Circuits and Systems for Video Technology最新文献

{"title":"LCSM: A Local Chart-Based Statistical Model for Femoral Shape Analysis","authors":"Lujian Zhang;Tianyu Fu;Dejin Yang;Jingyi Liu;Jiaju Zhang;Deqiang Xiao;Jingfan Fan;Danni Ai;Hong Song;Yixin Zhou;Jian Yang","doi":"10.1109/TCSVT.2026.3701217","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3701217","url":null,"abstract":"Statistical shape modeling of the femur is vital for clinical applications, yet linear methods struggle with complex geometric variations. While recent chart-based generative models like ChartPointFlow excel at general point cloud reconstruction, their direct application to the nuanced domain of SSM is limited. To bridge this gap, we propose a novel framework that adapts and significantly extends the chart-based paradigm for robust statistical shape analysis. Our core contributions are threefold. First, we are applying this substructure-based generative approach to the statistical modeling of anatomical shapes, moving beyond reconstruction. Second, to overcome the limitations of baseline encoders in capturing intricate anatomical details, we design a Geometric-enhanced Dynamic Graph Convolutional encoder that integrates multi-level geometric feature encoding. Third, to better learn the statistical distribution across a population, we introduce a point-wise feature augmentation module, termed Fine Feature Mixture module, which enriches feature diversity and robustness by fusing fine-grained representations across different samples. Extensive reconstruction and generation experiments on a femur dataset demonstrate our framework’s superiority. Furthermore, evaluations on multiple ShapeNet categories validate its generalization and applicability to diverse 3D shape modeling tasks. Code will be available in <uri>https://github.com/LujZhang/LCSM</uri>","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"14912-14928"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Cross-View Geo-Localization via Global–Local Feature Matching and Keypoint Guidance","authors":"Xuyi Fan;Hongguang Li;Yangzhu Wang;Huaxin Qiu;Miaomiao Liu;Wei Li;Li Shen","doi":"10.1109/TCSVT.2026.3704558","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3704558","url":null,"abstract":"Cross-view geo-localization (CVGL), which aims to match ground- or drone-view imagery with satellite maps, has emerged as a fundamental task in remote sensing, with broad applications in urban monitoring and uncrewed aerial vehicle (UAV) localization. Despite remarkable progress, existing approaches rely heavily on metric-based comparisons of global image features, which often leads to mismatches in regions with highly similar global appearances but distinct local structures. To address this limitation, this work introduces a novel CVGL framework, termed LKGL, which integrates two complementary methods. First, the global–local feature matching (GLFM) method computes similarity at both holistic and localized levels and integrates a reliability-based filtering mechanism to remove ambiguous or low-confidence local correspondences, thereby enhancing robustness against confusing global patterns. Second, the keypoint-guided feature pyramid network (KGFPN) integrates multiscale features with spatial priors derived from keypoint maps through attention-based interactions between shallow and deep layers. This design further enhances local feature expressiveness and structural sensitivity. Together, the GLFM and KGFPN enable LKGL to balance global contextual information with fine-grained local cues, achieving state-of-the-art performance on University-1652 and SUES-200. The source code is released on <uri>https://github.com/OrigamiSL/LKGL</uri>","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"14637-14649"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerial Vision-Language Navigation With a Unified Framework for Spatial, Temporal and Embodied Reasoning","authors":"Huilin Xu;Zhuoyang Liu;Yixiang Luomei;Feng Xu","doi":"10.1109/TCSVT.2026.3701286","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3701286","url":null,"abstract":"Aerial Vision-and-Language Navigation (VLN) aims to enable uncrewed aerial vehicles (UAVs) to interpret natural language instructions and navigate complex urban environments using onboard visual observations. This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery. Existing methods often rely on panoramic images, depth information, or odometry to support spatial reasoning and action planning. Such reliance on additional sensing modalities increases system cost and integration complexity. In this work, we present a unified aerial VLN framework that operates solely on egocentric monocular RGB observations and natural language instructions. The model formulates navigation as a next-token prediction problem, jointly optimizing spatial perception, trajectory reasoning, and action prediction through prompt-guided multi-task learning. Moreover, we propose a keyframe selection strategy to reduce visual redundancy by retaining semantically informative frames, along with an action merging and label reweighting mechanism that mitigates long-tailed supervision imbalance and facilitates stable multi-task co-training. Extensive experiments on the AerialVLN and OpenFly benchmarks validate the effectiveness of our method. Under the challenging monocular RGB-only setting, our model achieves strong results across both seen and unseen environments. It significantly outperforms existing monocular baselines and narrows the performance gap with state-of-the-art panoramic RGB-D counterparts. Comprehensive ablation studies further validate the contribution of our architectural design and choices. Our code is publicly available at <uri>https://github.com/return-sleep/AeroAct</uri>","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"14942-14957"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SRG-OSyNet: A Neuro-Oscillation Inspired Audio-Visual Network for Deepfake Detection","authors":"Zareena Amin;Badri Narayan Subudhi;Karan Nathwani;Vinit Jakhetiya","doi":"10.1109/TCSVT.2026.3705934","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3705934","url":null,"abstract":"Multimodal deepfakes pose an escalating challenge to reliable video authenticity analysis by jointly manipulating visual and auditory signals to generate highly realistic yet deceptive content. Although recent audio-visual deepfake detection methods leverage cross-modal correlations, most rely on shallow fusion strategies that inadequately capture long-range temporal dependencies and fine-grained spectral inconsistencies across modalities. Furthermore, the latent dynamical coupling that governs the coherent temporal evolution of audio and video streams is often overlooked. To address these limitations, this paper presents a Spectral-Resonance-Gated Oscillatory Synchrony Network (SRG-OSyNet) for multimodal deepfake detection, which explicitly models disruptions in audio-visual synchrony. The proposed framework incorporates a Feature-Driven Oscillatory Representation module to encode amplitude-frequency-phase interactions, a Dilated Attentive Temporal Convolutional Network to model long-term temporal dependencies, and a resonance-gating mechanism to strengthen cross-modal spectral alignment. In addition, contrastive multimodal alignment, spectral coherence regularization and oscillator stability constraints are introduced to further enhance the robustness and discriminative capacity of the learned representations. Experimental evaluations on the FakeAVCeleb and DF-TIMIT datasets demonstrate that the proposed method consistently outperforms state-of-the-art audio-visual deepfake detection approaches.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"15931-15945"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEFT-DPT: A Lightweight Multimodal Framework for Egocentric Video Action Recognition","authors":"Pawanesh Kumar Vishwakarma;Dushyant Kumar Singh;Abhimanyu Sahu","doi":"10.1109/TCSVT.2026.3707206","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3707206","url":null,"abstract":"Egocentric video analysis has emerged as an active and rapidly growing area of research in computer vision and multimedia due to its ability to capture human actions from a first-person perspective rich in visual and motion cues. This paper presents a lightweight multimodal framework for action localization and recognition in egocentric videos. Existing approaches often fail to effectively leverage complementary information from RGB, motion, and depth modalities, and even when multiple cues are fused, they typically lack adaptive integration, accurate localization, and scalability for large datasets. To overcome these challenges, we introduce the Dynamic Egocentric Feature Transformation (DEFT) module, which adaptively encodes features while preserving the distinctive motion and viewpoint characteristics of egocentric videos. DEFT emphasizes on central and action-relevant regions through spatial alignment and Gaussian-based weighting, enabling precise action localization and robust recognition. Furthermore, a lightweight Co-Attention module adaptively fuses complementary modality features through a cross-modal agreement. Next, we construct a Sparse Video Similarity Graph (SVSG) in the deep feature space using ResNet-50, where the central frame regions serve as graph nodes. To maintain computational efficiency, Dynamic Percentile Thresholding (DPT) retains only the most informative graph edges, yielding a sparse, yet meaningful graph representation. The resulting graph is then processed by a Graph Attention Network (GAT) for context-aware learning and action classification. Our lightweight framework, containing only 34.22M parameters, achieves 46.25% lower computation time compared to large-scale pretrained foundation models, while maintaining competitive accuracy. Extensive experiments on four benchmark egocentric video datasets—EPIC-Kitchens (RGB + Optical Flow), MECCANO (RGB + Depth), GTEA (RGB), and ADL (RGB)—demonstrate the superiority of the proposed method, showing consistent improvements in different modalities and settings. Our implementation and code are publicly available at <uri>https://github.com/pawanesh-mnnit/deft_dpt</uri>","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"15110-15123"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry-Aware Point Cloud Completion via Sharpen-Hypergraph Convolution and Equivariance Operators","authors":"Hailiang Ye;Jiayue Qiu;Feilong Cao;Jiye Liang;Ke Lu","doi":"10.1109/TCSVT.2026.3702072","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3702072","url":null,"abstract":"Deep learning has achieved remarkable success in point cloud completion. However, existing methods easily struggle with geometric variations, such as rotations, translations, and structural differences among points. Accordingly, this paper develops a geometric-aware framework integrated with novel sharpen-hypergraph convolution and equivariance operators (GA-SHE) for rigid point cloud completion, achieving more uniform and reliable results. This framework is built upon two key components: an equivariant feature extractor (EFE) in the encoder and a sharpen-hypergraph convolution-based coarse generation (SHCG) module in the decoder. The former constructs an equivariance convolution to learn equivariance feature representations to rotations and translations, effectively leveraging the intrinsic geometric structures of point clouds and contributing to point cloud completion. A theoretical analysis of the equivariance property of EFE is also provided. Moreover, in the SHCG module, a novel sharpen-hypergraph convolution operator is proposed, modeling the high-order structural difference relationships by encouraging the central node to diverge from its neighbors. Based on this operator, SHCG enables the network to effectively capture high-order geometric difference-aware features among points and generate more uniform point clouds. Experiments on various datasets demonstrate the effectiveness and competitive performance of GA-SHE. Especially, the results highlight the effectiveness of the sharpen-hypergraph convolution operator and confirm that the equivariance representations learned by EFE align with the theoretical analysis.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"14400-14415"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ERNet: Efficient Revealing of Perceptually Encrypted Images With Long-Range Dependency","authors":"Tianyi Si;Ying Yang;Hangcheng Liu;Shangwei Guo;Tianwei Zhang;Tao Xiang","doi":"10.1109/TCSVT.2026.3709784","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3709784","url":null,"abstract":"Perceptual encryption (PE) conceals sensitive visual content to protect image privacy, but it also raises the security risk of unauthorized visual content revealing. In particular, position-altering PE schemes jointly introduce local degradation and cross-region pixel rearrangements, resulting in complex local-region short-range and cross-region long-range pixel dependencies. Effective revealing requires the joint modeling of both types of dependencies. However, most existing learning-based revealing methods rely heavily on local modeling assumptions and are mainly effective for low-resolution perceptually encrypted images (PEIs). When extended to high-resolution settings, they often suffer from unsatisfactory revealing fidelity and prohibitively high computational cost. To address these challenges, we propose an efficient revealing network (ERNet) for high-resolution PEIs. Specifically, ERNet first maps the high-resolution images into compact visual semantic features through a cross-scale low-dimensional compressed representation (CLCR) module, which reduces the cost of subsequent global dependency modeling. Then, we design the lightweight long-range dependency modeling (LLDM) module, which consists of receptive field enhanced attention (RFEA) and channel-wise feature hierarchical distillation (CFHD). RFEA combines local and sparse attention to jointly capture short-range and long-range pixel dependencies, while CFHD performs hierarchical channel distillation to select and refine the most informative features. Finally, we introduce a progressive deconvolution high-resolution reconstruction (PDHR) module to progressively recover image structure and fine details. Extensive experiments demonstrate that the proposed ERNet achieves high-fidelity and efficient PEI revealing across different PE schemes, encryption strengths, and resolution scenarios.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"15775-15788"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Reversible and Identifiable Faces for Privacy Protection","authors":"Xianhan Zeng;Gaozhi Liu;Sheng Li;Xinpeng Zhang;Jian Liu;Zhenxing Qian","doi":"10.1109/TCSVT.2026.3710358","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3710358","url":null,"abstract":"Face anonymization, which aims to conceal a person’s identity in visual contents while preserving visual utility of anonymized faces, plays a critical role in privacy protection of visual data. The challenge of face anonymization is how to achieve a high protection level without compromising the usability of original data, which has yet to be well addressed in previous works. In this paper, we propose RIFT, a <bold>R</b>eversible and <bold>I</b>dentifiable <bold>F</b>ace <bold>T</b>ransformer to generate reversible and identifiable faces (RIFs) that meet this challenge. Our RIFs are visually and statistically different from original faces to ensure privacy, while being bound to virtual identities for direct authentication. Moreover, the original faces can be accurately recovered, enabling original identity tracing and better virtual identity management. Concretely, RIFT employs a normalizing flow to project original faces into a latent space, then reversibly transforms them into RIFs using condition embeddings derived from key-dependent latent. To support this, we introduce an attention-based conditional affine coupling layer to integrate identity control, and design a set of loss functions to enforce reversibility, anonymity, diversity, and identifiability. Experimental results show that RIFT significantly outperforms existing methods in anonymity, identifiability, reversibility, and robustness against malicious attacks.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"15848-15863"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight Steganalysis With Low Distortion for Countering Robust Image Steganography","authors":"Qianqian Zhang;Yinhong Huang;Duo Ma;Xiangyang Luo;Yuanyuan Ma;Ning Ruan","doi":"10.1109/TCSVT.2026.3701492","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3701492","url":null,"abstract":"Robust image steganography in social networks presents significant challenges for steganalysis. Existing methods exploit low payload visibility and resistance to evade conventional passive steganalysis, leading to high false-positive and missed detection rates, which undermines reliable verification and poses a significant security threat to cyberspace. Active steganalysis is designed to eliminate hidden information through controlled modifications. However, existing methods usually encounter challenges such as a decline in the visual quality of processed images, excessive training costs of the models, and dependence on the image size. All these have limited the practical application of active steganalysis. To address this issue, we propose a block-based multi-objective analysis framework, which integrates dual-block constraints with multi-objective loss optimization. This framework achieves adaptive image partitioning by dividing the image into non-overlapping <inline-formula> <tex-math>$2times 2$ </tex-math></inline-formula> pixel blocks. The partitioning simultaneously satisfies distortion boundary constraints and luminance preservation requirements. A multi-objective loss function is proposed to eliminate redundant information effectively while maintaining image visual clarity. The weights of loss function are set based on evolutionary optimization. Furthermore, the performance of the proposed method is enhanced by a linear-time complexity depth-first search algorithm, which minimizes the PSB loss under block-level constraints. The optimization process incorporates perturbation-based refinement to facilitate global convergence. Extensive experiments conducted on the BOSSBase-1.01 and CelebA datasets validate the effectiveness of the proposed framework in defending against robust image steganography prevalent in social networks. Evaluations across different payload settings indicate that the proposed method achieves an average PSNR improvement of approximately 34% over AO-Net across both BOSSBase-1.01 and CelebA datasets. Furthermore, the framework processes standard test images with a latency of 6.4ms, without requiring any preset image size.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"15685-15700"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Energy-Efficient State-Space Model Accelerator for Real-Time Video Understanding via Redundancy-Conscious Design","authors":"Youngjin Moon;Sangwoo Ha;Junha Ryu;Soyeon Kim;Donghyeon Han;Hoi-Jun Yoo","doi":"10.1109/TCSVT.2026.3706549","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3706549","url":null,"abstract":"This work introduces SLYTHERIN, a redundancy-conscious accelerator design that exposes and eliminates structural redundancy inherent in state-space model (SSM)-based video inference. Recent video models have increasingly adopted SSM to replace attention mechanisms, enabling linear time and space complexity in sequence length, well-suited for long-range video understanding. However, despite its algorithmic efficiency, its deployment on resource-constrained platforms remains challenging due to 1) slow inference latency caused by sequential SSM operations and 2) excessive energy consumption dominated by frequent external memory access (EMA), particularly for high-resolution, long-duration video streams. SSM-based video inference inherently exhibits multiple forms of structural redundancy stemming from sequential state updates and spatio-temporal input characteristics. To address these redundancies, SLYTHERIN instantiates three architectural mechanisms that jointly optimize computation and memory access. First, an orchestrated task allocator eliminates token-level redundancy by identifying and removing redundant and low-importance tokens through input-adaptive token selection. Second, a reformulated SSM core removes state update redundancy by reordering SSM operations and applying fixed-size block processing, enabling localized state updates with reduced memory traffic. Third, a patch data management unit mitigates EMA energy through difference-based bit-sliced compression that exploits inter-frame locality. Consequently, SLYTHERIN achieves 2.81 mJ/frame energy consumption while maintaining real-time performance at 275 FPS.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 10","pages":"14838-14850"},"PeriodicalIF":10.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}