2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第9页

(ML)2P-Encoder: On Exploration of Channel-Class Correlation for Multi-Label Zero-Shot Learning (ML) 2p -编码器:多标签零次学习的通道类相关研究

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.02285

Ziming Liu, Song Guo, Xiaocheng Lu, Jingcai Guo, Jiewei Zhang, Yue Zeng, Fushuo Huo

{"title":"(ML)2P-Encoder: On Exploration of Channel-Class Correlation for Multi-Label Zero-Shot Learning","authors":"Ziming Liu, Song Guo, Xiaocheng Lu, Jingcai Guo, Jiewei Zhang, Yue Zeng, Fushuo Huo","doi":"10.1109/CVPR52729.2023.02285","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.02285","url":null,"abstract":"Recent studies usually approach multi-label zeroshot learning (MLZSL) with visual-semantic mapping on spatial-class correlation, which can be computationally costly, and worse still, fails to capture fine-grained classspecific semantics. We observe that different channels may usually have different sensitivities on classes, which can correspond to specific semantics. Such an intrinsic channelclass correlation suggests a potential alternative for the more accurate and class-harmonious feature representations. In this paper, our interest is to fully explore the power of channel-class correlation as the unique base for MLZSL. Specifically, we propose a light yet efficient Multi-Label Multi-Layer Perceptron-based Encoder, dubbed (ML)2P-Encoder, to extract and preserve channel-wise semantics. We reorganize the generated feature maps into several groups, of which each of them can be trained independently with (ML)2P-Encoder. On top of that, a global groupwise attention module is further designed to build the multilabel specific class relationships among different classes, which eventually fulfills a novel Channel-Class Correlation MLZSL framework (C3-MLZSL)11Released code:github.com/simonzmliu/cvpr23_mlzsl. Extensive experiments on large-scale MLZSL benchmarks including NUS-WIDE and Open-Images-V4 demonstrate the superiority of our model against other representative state-of-the-art models.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114845268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Panoptic Video Scene Graph Generation 全景视频场景图生成

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.01791

Jingkang Yang, Wen-Hsiao Peng, Xiangtai Li, Zujin Guo, Liangyu Chen, Bo Li, Zheng Ma, Kaiyang Zhou, Wayne Zhang, Chen Change Loy, Ziwei Liu

引用次数: 8

Advancing Visual Grounding with Scene Knowledge: Benchmark and Method 用场景知识推进视觉基础:基准和方法

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.01444

Zhihong Chen, Ruifei Zhang, Yibing Song, Xiang Wan, Guanbin Li

{"title":"Advancing Visual Grounding with Scene Knowledge: Benchmark and Method","authors":"Zhihong Chen, Ruifei Zhang, Yibing Song, Xiang Wan, Guanbin Li","doi":"10.1109/CVPR52729.2023.01444","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.01444","url":null,"abstract":"Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study [27], where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of Scene Knowledge-guided Visual Grounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction; the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. The dataset and code are available at https://github.com/zhjohnchan/SK-VG.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117007559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning 联邦半监督学习的类平衡自适应伪标记

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.01563

Ming Li, Qingli Li, Yan Wang

{"title":"Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning","authors":"Ming Li, Qingli Li, Yan Wang","doi":"10.1109/CVPR52729.2023.01563","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.01563","url":null,"abstract":"This paper focuses on federated semi-supervised learning (FSSL), assuming that few clients have fully labeled data (labeled clients) and the training datasets in other clients are fully unlabeled (unlabeled clients). Existing methods attempt to deal with the challenges caused by not independent and identically distributed data (Non-IID) setting. Though methods such as sub-consensus models have been proposed, they usually adopt standard pseudo labeling or consistency regularization on unlabeled clients which can be easily influenced by imbalanced class distribution. Thus, problems in FSSL are still yet to be solved. To seek for a fundamental solution to this problem, we present Class Balanced Adaptive Pseudo Labeling (CBAFed), to study FSSL from the perspective of pseudo labeling. In CBAFed, the first key element is a fixed pseudo labeling strategy to handle the catastrophic forgetting problem, where we keep a fixed set by letting pass information of unlabeled data at the beginning of the unlabeled client training in each communication round. The second key element is that we design class balanced adaptive thresholds via considering the empirical distribution of all training data in local clients, to encourage a balanced training process. To make the model reach a better optimum, we further propose a residual weight connection in local supervised training and global model aggregation. Extensive experiments on five datasets demonstrate the superiority of CBAFed. Code will be available at https://github.com/minglllli/CBAFed.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117031836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion 疯狂:一个合成数据集的身体展示详细的逼真的动画运动

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.00843

Michael J. Black, Priyanka Patel, J. Tesch, Jinlong Yang

{"title":"BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion","authors":"Michael J. Black, Priyanka Patel, J. Tesch, Jinlong Yang","doi":"10.1109/CVPR52729.2023.00843","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.00843","url":null,"abstract":"We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is.tue.mpg.de/.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"389 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120866056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Layout-based Causal Inference for Object Navigation 基于布局的对象导航因果推理

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.01039

Sixian Zhang, Xinhang Song, Weijie Li, Yubing Bai, Xinyao Yu, Shuqiang Jiang

{"title":"Layout-based Causal Inference for Object Navigation","authors":"Sixian Zhang, Xinhang Song, Weijie Li, Yubing Bai, Xinyao Yu, Shuqiang Jiang","doi":"10.1109/CVPR52729.2023.01039","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.01039","url":null,"abstract":"Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor. However, when the layout gap is significant, the experience exerts a negative effect on navigation. Motivated by keeping the positive effect and removing the negative effect of the experience, we propose the layout-based soft Total Direct Effect (L-sTDE) framework based on the causal inference to adjust the prediction of the navigation policy. In particular, we propose to calculate the layout gap which is defined as the KL divergence between the posterior and the prior distribution of the object layout. Then the sTDE is proposed to appropriately control the effect of the experience based on the layout gap. Experimental results on AI2THOR, RoboTHOR, and Habitat demonstrate the effectiveness of our method. The code is available at https://github.com/sx-zhang/Layout-based-sTDE.git.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123602621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction 基于特征分解和边缘重建的伪装目标检测

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.02111

Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Z. Guo, Xiu Li

{"title":"Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction","authors":"Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Z. Guo, Xiu Li","doi":"10.1109/CVPR52729.2023.02111","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.02111","url":null,"abstract":"Camouflaged object detection (COD) aims to address the tough issue of identifying camouflaged objects visually blended into the surrounding backgrounds. COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries. Existing approaches to this problem have developed various techniques to mimic the human visual system. Albeit effective in many cases, these methods still struggle when camouflaged objects are so deceptive to the vision system. In this paper, we propose the FEature Decomposition and Edge Reconstruction (FEDER) model for COD. The FEDER model addresses the intrinsic similarity of foreground and background by decomposing the features into different frequency bands using learnable wavelets. It then focuses on the most informative bands to mine subtle cues that differentiate foreground and background. To achieve this, a frequency attention module and a guidance-based feature aggregation module are developed. To combat the ambiguous boundary problem, we propose to learn an auxiliary edge reconstruction task alongside the COD task. We design an ordinary differential equation-inspired edge reconstruction module that generates exact edges. By learning the auxiliary task in conjunction with the COD task, the FEDER model can generate precise prediction maps with accurate object boundaries. Experiments show that our FEDER model significantly outperforms state-of-the-art methods with cheaper computational and memory costs. The code will be available at https://github.com/ChunmingHe/FEDER.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122132639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos 基于类原型对比学习的多标签细粒度教育视频分类

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.01908

Rohit Gupta, Anirban Roy, Claire G. Christensen, Sujeong Kim, Sarah Gerard, Madeline Cincebeaux, Ajay Divakaran, Todd Grindal, M. Shah

{"title":"Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos","authors":"Rohit Gupta, Anirban Roy, Claire G. Christensen, Sujeong Kim, Sarah Gerard, Madeline Cincebeaux, Ajay Divakaran, Todd Grindal, M. Shah","doi":"10.1109/CVPR52729.2023.01908","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.01908","url":null,"abstract":"The recent growth in the consumption of online media by children during early childhood necessitates data-driven tools enabling educators to filter out appropriate educational content for young learners. This paper presents an approach for detecting educational content in online videos. We focus on two widely used educational content classes: literacy and math. For each class, we choose prominent codes (sub-classes) based on the Common Core Standards. For example, literacy codes include ‘letter names’, ‘letter sounds’, and math codes include ‘counting’, ‘sorting’. We pose this as a finegrained multilabel classification problem as videos can contain multiple types of educational content and the content classes can get visually similar (e.g., ‘letter names’vs ‘letter sounds’). We propose a novel class prototypes based supervised contrastive learning approach that can handle fine-grained samples associated with multiple labels. We learn a class prototype for each class and a loss function is employed to minimize the distances between a class prototype and the samples from the class. Similarly, distances between a class prototype and the samples from other classes are maximized. As the alignment between visual and audio cues are crucial for effective comprehension, we consider a multimodal transformer network to capture the interaction between visual and audio cues in videos while learning the embedding for videos. For evaluation, we present a dataset, APPROVE, employing educational videos from YouTube labeled with fine-grained education classes by education researchers. APPROVE consists of 193 hours of expert-annotated videos with 19 classes. The proposed approach outperforms strong baselines on APPROVE and other benchmarks such as Youtube-8M, and COIN. The dataset is available at https://nusci.csl.sri.com/project/APPROVE.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"348 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120878052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Instant Multi-View Head Capture through Learnable Registration 即时多视图头部捕获通过可学习的注册

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.00081

Timo Bolkart, Tianye Li, Michael J. Black

{"title":"Instant Multi-View Head Capture through Learnable Registration","authors":"Timo Bolkart, Tianye Li, Michael J. Black","doi":"10.1109/CVPR52729.2023.00081","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.00081","url":null,"abstract":"Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow and commonly address the problem in two separate steps; multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3D Meshes from Performances of Expressive Heads) to directly infer 3D heads in dense correspondence from calibrated multi-view images. Registering datasets of 3D scans typically requires manual parameter tuning to find the right balance between accurately fitting the scans' surfaces and being robust to scanning noise and outliers. Instead, we propose to jointly register a 3D head dataset while training TEMPEH. Specifically, during training, we minimize a geometric loss commonly used for surface registration, effectively leveraging TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric feature representation that samples and fuses features from each view using camera calibration information. To account for partial occlusions and a large capture volume that enables head movements, we use view-and surface-aware feature fusion, and a spatial transformer-based head localization module, respectively. We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3D heads in dense correspondence without requiring scans. Predicting one head takes about 0.3 seconds with a median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art. This enables the efficient capture of large datasets containing multiple people and diverse facial motions. Code, model, and data are publicly available at https://tempeh.is.tue.mpg.de.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125942936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks 速率梯度逼近攻击威胁深度尖峰神经网络

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2023-06-01 DOI: 10.1109/CVPR52729.2023.00763

Tong Bu, Jianhao Ding, Zecheng Hao, Zhaofei Yu

{"title":"Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks","authors":"Tong Bu, Jianhao Ding, Zecheng Hao, Zhaofei Yu","doi":"10.1109/CVPR52729.2023.00763","DOIUrl":"https://doi.org/10.1109/CVPR52729.2023.00763","url":null,"abstract":"Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware. State-of-the-art SNNs are typically composed of simple Leaky Integrate-and-Fire (LIF) neurons and have become comparable to ANNs in image classification tasks on large-scale datasets. However, the robustness of these deep SNNs has not yet been fully uncovered. In this paper, we first experimentally observe that layers in these SNNs mostly communicate by rate coding. Based on this rate coding property, we develop a novel rate coding SNN-specified attack method, Rate Gradient Approximation Attack (RGA). We generalize the RGA attack to SNNs composed of LIF neurons with different leaky parameters and input encoding by designing surrogate gradients. In addition, we develop the time-extended enhancement to generate more effective adversarial examples. The experiment results indicate that our proposed RGA attack is more effective than the previous attack and is less sensitive to neuron hyperparameters. We also conclude from the experiment that rate-coded SNN composed of LIF neurons is not secure, which calls for exploring training methods for SNNs composed of complex neurons and other neuronal codings. Code is available at https://github.com/putshua/SNN_attack_RGA","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126164295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0