Image and Vision Computing最新文献

{"title":"A multi-scale U-shaped transformer neural network for low-light image enhancement","authors":"Ji Soo Shin,&nbsp;Ho Sub Lee","doi":"10.1016/j.imavis.2025.105801","DOIUrl":"10.1016/j.imavis.2025.105801","url":null,"abstract":"<div><div>Low-light image enhancement (LLIE) aims to improve the visibility and visual quality of images captured under insufficient lighting conditions, which are typically characterized by low contrast, suppressed textures, and amplified noise. Recent methods often employ a multi-scale enhancement strategy by stacking sub-networks—such as cascaded convolutional blocks or a single scale transposed self-attention module—to refine contrast from coarse to fine levels. However, these methods struggle to effectively restore natural color appearance and fail to preserve global illumination cues, which limits the generalization capability of the models. In addition, conventional self-attention methods for LLIE operate at a single resolution, making it difficult to effectively fuse multi-scale features and thus constraining their ability to simultaneously capture long-range dependencies and preserve fine structural details. To address these issues, this paper proposes MSTSA-UTNet, a compact U-shaped Transformer architecture that incorporates a newly designed Transformer block based on multi-scale transposed self-attention (MSTSA) with lightweight feed forward modules, and adopts a multi-scale input, single-scale output (MISO) strategy. The key idea of MSTSA is to enable multi-resolution interaction by simultaneously incorporating original high-resolution features and down-sampled low-resolution features. Furthermore, the proposed feature extraction and fusion framework comprises two core components: a prior-guided shallow feature extraction (PG-SFE) module that preserves low-level spatial cues while incorporating illumination priors to modulate shallow features, and a multi-scale feed forward network (MSFFN) that performs gated fusion to selectively integrate global context and local detail. This design facilitates improved feature learning for low-light enhancement. Extensive experimental results demonstrate that the proposed MSTSA-UTNet consistently outperforms recent state-of-the-art multi-scale enhancement method, SMNet [<span><span>37</span></span>], by up to 0.59 dB in PSNR on the LOL-v1 dataset.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105801"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790258","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}

{"title":"MSBC-Segformer: An automatic segmentation model of clinical target volume and organs at risk in CT images for radiotherapy after breast-conserving surgery","authors":"Yadi Gao ,&nbsp;Qian Sun ,&nbsp;Lan Ye ,&nbsp;Chengliang Li ,&nbsp;Peipei Dang ,&nbsp;Min Han","doi":"10.1016/j.imavis.2025.105878","DOIUrl":"10.1016/j.imavis.2025.105878","url":null,"abstract":"<div><div>In breast-conserving surgery (BCS) radiotherapy for breast cancer (BC), clinical target volume (CTV) and organs at risk (OARs) on CT images are mainly manually delineated layer by layer by radiation oncologists (RO), a time-consuming process prone to variability due to clinical experience differences and inter- and intra-observer variations. To address this, we developed a new automatic delineation model aimed at medical CT images, specifically for computer-assisted medical detection and diagnosis. The CT scans of 100 patients who underwent BCS and radiotherapy were collected. These data were used to create, train, and validate a new deep-learning (DL) model, the MSBC-Segformer (Multi-Scale Boundary-Constrained Segmentation Model Based on Transformer) model, which was proposed to automatically segment the CTV and OARs. The Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (95HD) were used to evaluate the effectiveness of the proposed model. In result, the MSBC-Segformer model can provide accurate and efficient delineation of CTV and OARs for BC patients underwent radiotherapy after BCS, outperforming both junior doctors and almost all other existing CNN models, and reducing the instability of segmentation results due to observer differences, thus significantly enhancing clinical efficiency. Moreover, evaluation by three ROs revealed no significant difference between the model and manual delineation by the senior doctors (<span><math><mrow><mi>p</mi><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>98</mn></mrow></math></span> for CTV and <span><math><mrow><mi>p</mi><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>59</mn></mrow></math></span> for OARs). The model significantly reduced segmentation time, with an average of only 12.53 s per patient.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105878"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790260","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}

{"title":"WAM-Net: Wavelet-Based Adaptive Multi-scale Fusion Network for fine-grained action recognition","authors":"Jirui Di,&nbsp;Zhengping Hu,&nbsp;Hehao Zhang,&nbsp;Qiming Zhang,&nbsp;Zhe Sun","doi":"10.1016/j.imavis.2025.105855","DOIUrl":"10.1016/j.imavis.2025.105855","url":null,"abstract":"<div><div>Fine-grained actions often lack scene prior information, making strong temporal modeling particularly important. Since these actions primarily rely on subtle and localized motion differences, single-scale features are often insufficient to capture their complexity. In contrast, multi-scale features not only capture fine-grained patterns but also contain rich rhythmic information, which is crucial for modeling temporal dependencies. However, existing methods for processing multi-scale features suffer from two major limitations: they often rely on naive downsampling operations for scale alignment, causing significant structural information loss, and they treat features from different layers equally, without fully exploiting the complementary strengths across hierarchical levels. To address these issues, we propose a novel Wavelet-Based Adaptive Multi-scale Fusion Network (WAM-Net), which consists of three key components: (1) a Wavelet-based Fusion Module (WFM) that achieves feature alignment through wavelet reconstruction, avoiding the structural degradation typically introduced by direct downsampling, (2) an Adaptive Feature Selection Module (AFSM) that dynamically selects and fuses two levels of features based on global information, enabling the network to leverage their complementary advantages, and (3) a Duration Context Encoder (DCE) that extracts temporal duration representations from the overall video length to guide global dependency modeling. Extensive experiments on Diving48, FineGym, and Kinetics-400 demonstrate that our approach consistently outperforms existing state-of-the-art methods.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105855"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685265","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}

{"title":"Enhancing Zero-Shot Object-Goal Visual Navigation with target context and appearance awareness","authors":"Yu Fu ,&nbsp;Lichun Wang ,&nbsp;Tong Bie ,&nbsp;Shuang Li ,&nbsp;Tong Gao ,&nbsp;Baocai Yin","doi":"10.1016/j.imavis.2025.105873","DOIUrl":"10.1016/j.imavis.2025.105873","url":null,"abstract":"<div><div>The Zero-Shot Object-Goal Visual Navigation (ZSON) task focuses on leveraging the target semantic information to transfer the end-to-end navigation policy learned from seen classes to unseen classes. For most ZSON methods, the target label serves as the primary source of semantic information. However, the learning of navigation policies based on the single semantic clue limits the transferability of navigation policies. Inspired by the phenomenon that humans associate object labels during searching for targets, we propose the Dual Target Awareness Network (DTAN), which expands the label semantic to target context and target appearance, providing more target clues for the navigation policy learning. By using Large Language Model (LLM), DTAN first infers target context and target attribute based on the target label. The target context is encoded and then interacts with the observation to obtain the context-aware feature. The target attribute is used to generate target appearance and then interacts with the observation to obtain the appearance-aware feature. By fusing the two kinds of features, the target-aware feature is obtained and fed into the policy network to make action decisions. Experimental results demonstrate that DTAN outperforms the state-of-the-art ZSON method by 6.9% in Success Rate (SR) and 3.1% in Success weighted by Path Length (SPL) for unseen targets on AI2-THOR simulator. Experiments conducted on RoboTHOR and Habitat (MP3D) simulators further prove the scalability of DTAN to larger-scale and more realistic scenes.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105873"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840005","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}

{"title":"BSA-Dehaze: Multi-Scale Bitemporal Fusion and Size-Aware Decoder for Unsupervised Image Dehazing","authors":"Wujin Li ,&nbsp;Qian Xing ,&nbsp;Wei He ,&nbsp;Longyuan Guo ,&nbsp;Jianhui Wu ,&nbsp;Minzhi Zhao ,&nbsp;Siyuan Chen","doi":"10.1016/j.imavis.2025.105819","DOIUrl":"10.1016/j.imavis.2025.105819","url":null,"abstract":"<div><div>Single-image dehazing plays a critical role in various autonomous vision systems. Early methods relied on hand-crafted optimization techniques, whereas recent approaches leverage deep neural networks trained on synthetic data, owing to the scarcity of real-world paired datasets. However, this often results in domain bias when applied to outdoor scenes. In this paper, we present BSA-Dehaze, an unsupervised single-image dehazing framework that integrates a Multi-Scale Bitemporal Fusion Module (MBFM) and a Size-Aware Decoder (SA-Decoder). The method operates without requiring ground-truth images. Our method reformulates dehazing as a haze-to-clear image translation task. BSA-Dehaze incorporates a novel Encoder-SA-Decoder built with ResNet blocks, designed to better preserve image details and edge sharpness. To enhance feature fusion and training efficiency, we introduce the MBFM. A multi-scale discriminator (MSD) is proposed, along with Hinge Loss and Dynamic Block-wise Contrastive Loss, to improve training stability and emphasize challenging samples. Ablation studies verify the contribution of each component. Experimental results on SOTS outdoor, BeDDE, and a real-world dataset demonstrate that our method surpasses existing approaches in both performance and efficiency, despite being trained on significantly less data.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105819"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600398","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}

{"title":"A computationally efficient framework leveraging auxiliary head features for robust cloth-changing person re-identification","authors":"Shanna Zhuang,&nbsp;Yining Li,&nbsp;Jiaxin Zhang,&nbsp;Jing Bai,&nbsp;Congcong Li,&nbsp;Zhengyou Wang","doi":"10.1016/j.imavis.2025.105852","DOIUrl":"10.1016/j.imavis.2025.105852","url":null,"abstract":"<div><div>Cloth-Changing Person Re-identification (CC-ReID) addresses the critical challenge of matching individuals across surveillance cameras after clothing changes. This task presents unique difficulties due to appearance variations that increase visual ambiguity among persons. While existing approaches predominantly concentrate on body shape analysis, contour sketches, and human parsing techniques, these methods exhibit limitations in robustness while demanding substantial computational resources. To exploit cloth-irrelevant information for the CC-ReID task at low cost, a computationally efficient framework that effectively leverages head features as discriminative auxiliary cues is proposed in this paper. First, a Head Region Cropping and Location (HRCL) module is presented to isolate head regions, enabling rough feature localization. Second, a lightweight Head Attention Network (HANet) is presented that integrates MobileNet architecture with cascaded channel-spatial attention mechanisms, which synergistically capture both global semantic patterns and local discriminative characteristics. This dual-stream design achieves effective feature enhancement without requiring complex auxiliary feature extraction as required by conventional approaches. An optimal Feature-Level Fusion Module (FLFM) that combines learned head representations with conventional body features is presented. Extensive evaluations on the LTCC and PRCC datasets demonstrate significant performance improvements. The proposed method achieves state-of-the-art Rank-1 accuracy of 58.8% and a competitive mAP of 25.4% on the LTCC dataset, while attaining 59.8% Rank-1 accuracy and 57.9% mAP on PRCC. Comprehensive ablation studies further validate the effectiveness of each proposed component.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105852"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618475","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}

{"title":"MVD-NeRF: Multi-View Deblurring Neural Radiance Fields from Defocused Images","authors":"Zhenyu Yin ,&nbsp;Xiaohui Wang ,&nbsp;Feiqing Zhang ,&nbsp;Xiaoqiang Shi ,&nbsp;Dan Feng","doi":"10.1016/j.imavis.2025.105876","DOIUrl":"10.1016/j.imavis.2025.105876","url":null,"abstract":"<div><div>Neural Radiance Fields (NeRF) have demonstrated exceptional three-dimensional (3D) reconstruction quality by synthesizing novel views from multi-view images. However, NeRF algorithms typically require clear and static images to function effectively, and little attention has been given to suboptimal scenarios involving noise such as reflections and blur. Although blurred images are common in real-world situations, few studies have explored NeRF for handling blur, particularly defocus blur. Correctly simulating the formation of defocus blur is the key to deblurring and helps to accurately synthesize new perspectives from blurred images. Therefore, this paper proposes a Multi-View Deblurring Neural Radiance Fields from Defocused Images (MVD-NeRF), a framework for 3D reconstruction from defocus-blurred images. The framework ensures consistency in 3D geometry and appearance by modeling the formation of defocus blur. MVD-NeRF introduces the Defocus Modeling Approach (DMA), a novel method for simulating defocused scenes. When the view is fixed, DMA assumes that the pixel is rendered by multiple rays emitted from the same light source. Additionally, MVD-NeRF proposes a new Multi-view Panning Algorithm (MPA), which simulates light source movement through slight shifts in the camera center across different views, thereby generating blur effects similar to those in real photography. Together, DMA and MPA enhance MVD-NeRF’s ability to capture intricate scene details. Our experimental results validate that MVD-NeRF achieves significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The source code for MVD-NeRF is available at the following URL: <span><span>https://github.com/luckhui0505/MVD-NeRF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105876"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790257","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}

{"title":"When Mamba meets CNN: A hybrid architecture for skin lesion segmentation","authors":"Yun Xiao ,&nbsp;Caijuan Shi ,&nbsp;Jinghao Jia ,&nbsp;Ao Cai ,&nbsp;Yinan Zhang ,&nbsp;Meiwen Zhang","doi":"10.1016/j.imavis.2025.105880","DOIUrl":"10.1016/j.imavis.2025.105880","url":null,"abstract":"<div><div>As an important means of computer-aided diagnosis and treatment of skin cancer, skin lesion segmentation has recently received extensive research based on different deep models. Because of the limitations of each single deep model, the hybrid architectures, especially the Mamba–CNN based methods, have become a research hotspot. However, the segmentation accuracy of existing Mamba–CNN methods is still limited, especially for lesions of varying sizes and blurry boundaries, meanwhile, the model’s computational complexity remains high. Therefore, to address these issues and improve the skin lesion segmentation performance, we propose a new Mamba–CNN based model, named Feature Fusion and Boundary Awareness Visual Mamba (FFBA-VM). Specifically, the designed Multi-scale Hybrid Attention Interaction (MHAI) module can enhance the multi-scale feature representation with the powerful capability of long-range dependency modeling to obtain rich local and global information. The designed Region Localization and Boundary Enhancement (RLBE) module can effectively explore the local information to alleviate inaccurate skin lesion localization and boundary blurring. The Lightweight Visual State Space (LVSS) module is designed to reduce the model’s computational complexity. Extensive experiments are conducted on four datasets, and our model FFBA-VM effectively boosts the segmentation accuracy in terms of multiple evaluation metrics. For example, FFBA-VM achieves mIoU and DSC of 80.28% and 89.06% on the ISIC17 dataset, and reaches mIoU and DSC of 80.47% and 89.17% on the ISIC18 dataset. The experimental results indicate that our proposed FFBA-VM can outperform the existing state-of-the-art methods, validating its effectiveness and practicality for skin lesion segmentation.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105880"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790261","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}

{"title":"LoGA-Attack: Local geometry-aware adversarial attack on 3D point clouds","authors":"Jia Yuan ,&nbsp;Jun Chen ,&nbsp;Chongshou Li ,&nbsp;Pedro Alonso ,&nbsp;Xinke Li ,&nbsp;Tianrui Li","doi":"10.1016/j.imavis.2025.105871","DOIUrl":"10.1016/j.imavis.2025.105871","url":null,"abstract":"<div><div>Adversarial attacks on 3D point clouds are increasingly critical for safety-sensitive domains like autonomous driving. Most existing methods ignore local geometric structure, yielding perturbations that harm imperceptibility and geometric consistency. We introduce local geometry-aware adversarial attack (LoGA-Attack), a local geometry-aware approach that exploits topological and geometric cues to craft refined perturbations. A Neighborhood Centrality (NC) score partitions points into contour and flat points sets. Contour points receive gradient-based iterative updates to maximize attack strength, while flat points use an Optimal Neighborhood-based Attack (ONA) that projects gradients onto the most consistent local geometric direction. Experiments on ModelNet40 and ScanObjectNN show higher attack success with lower perceptual distortion, demonstrating superior performance and strong transferability. Our code is available at: <span><span>https://github.com/yuanjiachn/LoGA-Attack</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105871"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790259","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}

{"title":"CONXA: A CONvnext and CROSS-attention combination network for Semantic Edge Detection","authors":"Gwangsoo Kim ,&nbsp;Hyuk-jae Lee ,&nbsp;Hyunmin Jung","doi":"10.1016/j.imavis.2025.105867","DOIUrl":"10.1016/j.imavis.2025.105867","url":null,"abstract":"<div><div>Semantic Edge Detection (SED) is an advanced edge detection technique that simultaneously detects edges in an image and classifies them according to their semantics. It is expected to be applied in various fields, such as medical imaging, satellite imagery, and smart manufacturing. Although previous research on SED has significantly improved performance, further advancements are still needed. In particular, existing studies have typically focused on specific types of dataset, limiting the broader applicability of SED techniques. Motivated by this, our paper makes three key contributions. First, we propose a novel network for SED, called CONXA. CONXA improves SED accuracy by leveraging the powerful feature extraction of ConvNeXt and the effective feature combination of cross-attention. Second, we introduce a novel loss function, Inverted Dice (I-Dice) loss, which calculates loss based on a sufficient number of non-edge pixels rather than edge pixels. This helps balance false positives and false negatives, enabling more stable training. Third, unlike previous studies that typically use only one type of dataset, we validate our method using two distinct types of datasets commonly used in SED. Experimental results demonstrate that our approach significantly outperforms existing state-of-the-art (SOTA) methods on datasets that define semantics by edge types, and achieves comparable performance to SOTA methods on datasets where semantics are defined by object boundaries. This indicates that our method can be effectively applied across diverse datasets regardless of the semantic characteristics of edges, contributing to the generalization of SED. Code is available at <span><span>https://github.com/GSKIM13/CONXA/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105867"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790262","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}