Computer Vision and Image Understanding最新文献_第10页

A MLP architecture fusing RGB and CASSI for computational spectral imaging 融合 RGB 和 CASSI 的 MLP 架构，用于计算光谱成像

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-25 DOI: 10.1016/j.cviu.2024.104214

Zeyu Cai , Ru Hong , Xun Lin , Jiming Yang , YouLiang Ni , Zhen Liu , Chengqian Jin , Feipeng Da

{"title":"A MLP architecture fusing RGB and CASSI for computational spectral imaging","authors":"Zeyu Cai , Ru Hong , Xun Lin , Jiming Yang , YouLiang Ni , Zhen Liu , Chengqian Jin , Feipeng Da","doi":"10.1016/j.cviu.2024.104214","DOIUrl":"10.1016/j.cviu.2024.104214","url":null,"abstract":"<div><div>The coded Aperture Snapshot Spectral Imaging (CASSI) system offers significant advantages in dynamically acquiring hyper-spectral images compared to traditional measurement methods. However, it faces the following challenges: (1) Traditional masks rely on random patterns or analytical design, limiting CASSI’s performance improvement. (2) Existing CASSI reconstruction algorithms do not fully utilize RGB information. (3) High-quality reconstruction algorithms are often slow and limited to offline scene reconstruction. To address these issues, this paper proposes a new MLP architecture, Spectral–Spatial MLP (SSMLP), which replaces the transformer structure with a network using CASSI measurements and RGB as multimodal inputs. This maintains reconstruction quality while significantly improving reconstruction speed. Additionally, we constructed a teacher-student network (SSMLP with a teacher, SSMLP-WT) to transfer the knowledge learned from a large model to a smaller network, further enhancing the smaller network’s accuracy. Extensive experiments show that SSMLP matches the performance of transformer-based structures in spectral image reconstruction while improving inference speed by at least 50%. The reconstruction quality of SSMLP-WT is further improved by knowledge transfer without changing the network, and the teacher boosts the performance by 0.92 dB (44.73 dB vs. 43.81 dB).</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104214"},"PeriodicalIF":4.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587209","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 GCN and Transformer complementary network for skeleton-based action recognition 用于基于骨骼的动作识别的 GCN 和 Transformer 互补网络

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-22 DOI: 10.1016/j.cviu.2024.104213

Xuezhi Xiang , Xiaoheng Li , Xuzhao Liu , Yulong Qiao , Abdulmotaleb El Saddik

{"title":"A GCN and Transformer complementary network for skeleton-based action recognition","authors":"Xuezhi Xiang , Xiaoheng Li , Xuzhao Liu , Yulong Qiao , Abdulmotaleb El Saddik","doi":"10.1016/j.cviu.2024.104213","DOIUrl":"10.1016/j.cviu.2024.104213","url":null,"abstract":"<div><div>Graph Convolution Networks (GCNs) have been widely used in skeleton-based action recognition. Although there are significant progress, the inherent limitation still lies in the restricted receptive field of GCN, hindering its ability to extract global dependencies effectively. And the joints that are structurally separated can also have strong correlation. Previous works rarely explore local and global correlations of joints, leading to insufficiently model the complex dynamics of skeleton sequences. To address this issue, we propose a GCN and Transformer complementary network (GTC-Net) that allows parallel communications between GCN and Transformer domains. Specifically, we introduce a graph convolution and self-attention combined module (GAM), which can effectively leverage the complementarity of GCN and self-attention to perceive local and global dependencies of joints for the human body. Furthermore, in order to address the problems of long-term sequence ordering and position detection, we design a position-aware module (PAM), which can explicitly capture the ordering information and unique identity information for body joints of skeleton sequence. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets are conducted to evaluate our proposed method. The results demonstrate that our method can achieve competitive results on both datasets.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104213"},"PeriodicalIF":4.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528589","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

Reverse Stable Diffusion: What prompt was used to generate this image? 反向稳定扩散：生成这张图片时使用了什么提示？

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-19 DOI: 10.1016/j.cviu.2024.104210

Florinel-Alin Croitoru , Vlad Hondru , Radu Tudor Ionescu , Mubarak Shah

{"title":"Reverse Stable Diffusion: What prompt was used to generate this image?","authors":"Florinel-Alin Croitoru , Vlad Hondru , Radu Tudor Ionescu , Mubarak Shah","doi":"10.1016/j.cviu.2024.104210","DOIUrl":"10.1016/j.cviu.2024.104210","url":null,"abstract":"<div><div>Text-to-image diffusion models have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the generative process and how to engineer prompts in order to obtain the desired images. To this end, we study the task of predicting the prompt embedding given an image generated by a generative diffusion model. We consider a series of white-box and black-box models (with and without access to the weights of the diffusion network) to deal with the proposed task. We propose a novel learning framework comprising a joint prompt regression and multi-label vocabulary classification objective that generates improved prompts. To further improve our method, we employ a curriculum learning procedure that promotes the learning of image-prompt pairs with lower labeling noise (<em>i</em>.<em>e</em>. that are better aligned). We conduct experiments on the DiffusionDB data set, predicting text prompts from images generated by Stable Diffusion. In addition, we make an interesting discovery: training a diffusion model on the prompt generation task can make the model generate images that are much better aligned with the input prompts, when the model is directly reused for text-to-image generation. Our code is publicly available for download at <span><span>https://github.com/CroitoruAlin/Reverse-Stable-Diffusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104210"},"PeriodicalIF":4.3,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528466","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

Invisible backdoor attack with attention and steganography 利用注意力和隐写术进行隐形后门攻击

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-19 DOI: 10.1016/j.cviu.2024.104208

Wenmin Chen, Xiaowei Xu, Xiaodong Wang, Huasong Zhou, Zewen Li, Yangming Chen

{"title":"Invisible backdoor attack with attention and steganography","authors":"Wenmin Chen, Xiaowei Xu, Xiaodong Wang, Huasong Zhou, Zewen Li, Yangming Chen","doi":"10.1016/j.cviu.2024.104208","DOIUrl":"10.1016/j.cviu.2024.104208","url":null,"abstract":"<div><div>Recently, with the development and widespread application of deep neural networks (DNNs), backdoor attacks have posed new security threats to the training process of DNNs. Backdoor attacks on neural networks undermine the security and trustworthiness of DNNs by implanting hidden, unauthorized triggers, leading to benign behavior on clean samples while exhibiting malicious behavior on samples containing backdoor triggers. Existing backdoor attacks typically employ triggers that are sample-agnostic and identical for each sample, resulting in poisoned images that lack naturalness and are ineffective against existing backdoor defenses. To address these issues, this paper proposes a novel stealthy backdoor attack, where the backdoor trigger is dynamic and specific to each sample. Specifically, we leverage spatial attention on images and pre-trained models to obtain dynamic triggers, which are then injected using an encoder–decoder network. The design of the injection network benefits from recent advances in steganography research. To demonstrate the effectiveness of the proposed steganographic network, we design two backdoor attack modes named ASBA and ATBA, where ASBA utilizes the steganographic network for attack, while ATBA is a backdoor attack without steganography. Subsequently, we conducted attacks on Deep Neural Networks (DNNs) using four standard datasets. Our extensive experiments show that ASBA surpasses ATBA in terms of stealthiness and resilience against current defensive measures. Furthermore, both ASBA and ATBA demonstrate superior attack efficiency.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104208"},"PeriodicalIF":4.3,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528462","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

NeRFtrinsic Four: An end-to-end trainable NeRF jointly optimizing diverse intrinsic and extrinsic camera parameters NeRFtrinsic Four：端到端可训练 NeRF，联合优化各种内在和外在相机参数

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-18 DOI: 10.1016/j.cviu.2024.104206

Hannah Schieber , Fabian Deuser , Bernhard Egger , Norbert Oswald , Daniel Roth

{"title":"NeRFtrinsic Four: An end-to-end trainable NeRF jointly optimizing diverse intrinsic and extrinsic camera parameters","authors":"Hannah Schieber , Fabian Deuser , Bernhard Egger , Norbert Oswald , Daniel Roth","doi":"10.1016/j.cviu.2024.104206","DOIUrl":"10.1016/j.cviu.2024.104206","url":null,"abstract":"<div><div>Novel view synthesis using neural radiance fields (NeRF) is the state-of-the-art technique for generating high-quality images from novel viewpoints. Existing methods require a priori knowledge about extrinsic and intrinsic camera parameters. This limits their applicability to synthetic scenes, or real-world scenarios with the necessity of a preprocessing step. Current research on the joint optimization of camera parameters and NeRF focuses on refining noisy extrinsic camera parameters and often relies on the preprocessing of intrinsic camera parameters. Further approaches are limited to cover only one single camera intrinsic. To address these limitations, we propose a novel end-to-end trainable approach called NeRFtrinsic Four. We utilize Gaussian Fourier features to estimate extrinsic camera parameters and dynamically predict varying intrinsic camera parameters through the supervision of the projection error. Our approach outperforms existing joint optimization methods on LLFF and BLEFF. In addition to these existing datasets, we introduce a new dataset called iFF with varying intrinsic camera parameters. NeRFtrinsic Four is a step forward in joint optimization NeRF-based view synthesis and enables more realistic and flexible rendering in real-world scenarios with varying camera parameters.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104206"},"PeriodicalIF":4.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528464","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

Lightweight cross-modal transformer for RGB-D salient object detection 用于 RGB-D 突出物体检测的轻量级跨模态变换器

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-17 DOI: 10.1016/j.cviu.2024.104194

Nianchang Huang , Yang Yang , Qiang Zhang , Jungong Han , Jin Huang

{"title":"Lightweight cross-modal transformer for RGB-D salient object detection","authors":"Nianchang Huang , Yang Yang , Qiang Zhang , Jungong Han , Jin Huang","doi":"10.1016/j.cviu.2024.104194","DOIUrl":"10.1016/j.cviu.2024.104194","url":null,"abstract":"<div><div>Recently, Transformer-based RGB-D salient object detection (SOD) models have pushed the performance to a new level. However, they come at the cost of consuming abundant resources, including memory and power, thus hindering their real-life applications. To remedy this situation, a novel lightweight cross-modal Transformer (LCT) for RGB-D SOD will be presented in this paper. Specifically, LCT will first reduce its parameters and computational costs by employing a middle-level feature fusion structure and taking a lightweight Transformer as the backbone. Then, with the aid of Transformers, it will compensate for performance degradation by effectively capturing the cross-modal and cross-level complementary information from the multi-modal input images. To this end, a cross-modal enhancement and fusion module (CEFM) with a lightweight channel-wise cross attention block (LCCAB) will be designed to capture the cross-modal complementary information effectively but with fewer costs. A bi-directional multi-level feature interaction module (Bi-MFIM) with a lightweight spatial-wise cross attention block (LSCAB) will be designed to capture the cross-level complementary context information. By virtue of CEFM and Bi-MFIM, the performance degradation caused by parameter reduction can be well compensated, thus boosting the performances. By doing so, our proposed model has only 2.8M parameters with 7.6G FLOPs and runs at 66 FPS. Furthermore, experimental results on several benchmark datasets show that our proposed model can achieve competitive or even better results than other models. Our code will be released on <span><span>https://github.com/nexiakele/lightweight-cross-modal-Transformer-LCT-for-RGB-D-SOD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104194"},"PeriodicalIF":4.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528465","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

PMGNet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning PMGNet：互不纠缠和纠缠互利，促进合成零点学习

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-16 DOI: 10.1016/j.cviu.2024.104197

Yu Liu , Jianghao Li , Yanyi Zhang , Qi Jia , Weimin Wang , Nan Pu , Nicu Sebe

{"title":"PMGNet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning","authors":"Yu Liu , Jianghao Li , Yanyi Zhang , Qi Jia , Weimin Wang , Nan Pu , Nicu Sebe","doi":"10.1016/j.cviu.2024.104197","DOIUrl":"10.1016/j.cviu.2024.104197","url":null,"abstract":"<div><div>Compositional zero-shot learning (CZSL) aims to model compositions of two primitives (i.e., attributes and objects) to classify unseen attribute-object pairs. Most studies are devoted to integrating disentanglement and entanglement strategies to circumvent the trade-off between contextuality and generalizability. Indeed, the two strategies can mutually benefit when used together. Nevertheless, they neglect the significance of developing mutual guidance between the two strategies. In this work, we take full advantage of guidance from disentanglement to entanglement and vice versa. Additionally, we propose exploring multi-scale feature learning to achieve fine-grained mutual guidance in a progressive framework. Our approach, termed Progressive Mutual Guidance Network (PMGNet), unifies disentanglement–entanglement representation learning, allowing them to learn from and teach each other progressively in one unified model. Furthermore, to alleviate overfitting recognition on seen pairs, we adopt a relaxed cross-entropy loss to train PMGNet, without an increase of time and memory cost. Extensive experiments on three benchmarks demonstrate that our method achieves distinct improvements, reaching state-of-the-art performance. Moreover, PMGNet exhibits promising performance under the most challenging open-world CZSL setting, especially for unseen pairs.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104197"},"PeriodicalIF":4.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445585","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

FTM: The Face Truth Machine—Hand-crafted features from micro-expressions to support lie detection FTM：面部真实机器--从微表情中手工创建特征，支持谎言检测

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-16 DOI: 10.1016/j.cviu.2024.104188

Maria De Marsico, Giordano Dionisi, Donato Francesco Pio Stanco

{"title":"FTM: The Face Truth Machine—Hand-crafted features from micro-expressions to support lie detection","authors":"Maria De Marsico, Giordano Dionisi, Donato Francesco Pio Stanco","doi":"10.1016/j.cviu.2024.104188","DOIUrl":"10.1016/j.cviu.2024.104188","url":null,"abstract":"<div><div>This work deals with the delicate task of lie detection from facial dynamics. The proposed Face Truth Machine (FTM) is an intelligent system able to support a human operator without any special equipment. It can be embedded in the present infrastructures for forensic investigation or whenever it is required to assess the trustworthiness of responses during an interview. Due to its flexibility and its non-invasiveness, it can overcome some limitations of present solutions. Of course, privacy issues may arise from the use of such systems, as often underlined nowadays. However, it is up to the utilizer to take these into account and make fair use of tools of this kind. The paper will discuss particular aspects of the dynamic analysis of face landmarks to detect lies. In particular, it will delve into the behavior of the features used for detection and how these influence the system’s final decision. The novel detection system underlying the Face Truth Machine is able to analyze the subject’s expressions in a wide range of poses. The results of the experiments presented testify to the potential of the proposed approach and also highlight the very good results obtained in cross-dataset testing, which usually represents a challenge for other approaches.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104188"},"PeriodicalIF":4.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528463","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

M3A: A multimodal misinformation dataset for media authenticity analysis M3A：用于媒体真实性分析的多模态错误信息数据集

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-15 DOI: 10.1016/j.cviu.2024.104205

Qingzheng Xu , Huiqiang Chen , Heming Du , Hu Zhang , Szymon Łukasik , Tianqing Zhu , Xin Yu

{"title":"M3A: A multimodal misinformation dataset for media authenticity analysis","authors":"Qingzheng Xu , Huiqiang Chen , Heming Du , Hu Zhang , Szymon Łukasik , Tianqing Zhu , Xin Yu","doi":"10.1016/j.cviu.2024.104205","DOIUrl":"10.1016/j.cviu.2024.104205","url":null,"abstract":"<div><div>With the development of various generative models, misinformation in news media becomes more deceptive and easier to create, posing a significant problem. However, existing datasets for misinformation study often have limited modalities, constrained sources, and a narrow range of topics. These limitations make it difficult to train models that can effectively combat real-world misinformation. To address this, we propose a comprehensive, large-scale Multimodal Misinformation dataset for Media Authenticity Analysis (<span><math><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup><mi>A</mi></mrow></math></span>), featuring broad sources and fine-grained annotations for topics and sentiments. To curate <span><math><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup><mi>A</mi></mrow></math></span>, we collect genuine news content from 60 renowned news outlets worldwide and generate fake samples using multiple techniques. These include altering named entities in texts, swapping modalities between samples, creating new modalities, and misrepresenting movie content as news. <span><math><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup><mi>A</mi></mrow></math></span> contains 708K genuine news samples and over 6M fake news samples, spanning text, images, audio, and video. <span><math><mrow><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup><mi>A</mi></mrow></math></span> provides detailed multi-class labels, crucial for various misinformation detection tasks, including out-of-context detection and deepfake detection. For each task, we offer extensive benchmarks using state-of-the-art models, aiming to enhance the development of robust misinformation detection systems.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"249 ","pages":"Article 104205"},"PeriodicalIF":4.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445584","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

Region-aware image-based human action retrieval with transformers 利用变换器进行基于区域感知图像的人体动作检索

IF 4.3 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2024-10-14 DOI: 10.1016/j.cviu.2024.104202

Hongsong Wang , Jianhua Zhao , Jie Gui

引用次数: 0