检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

17,661 篇 会议
259 册 图书
190 篇 期刊文献
1 篇 学位论文

馆藏范围

18,110 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

10,534 篇 工学
- 6,233 篇 计算机科学与技术...
- 4,015 篇 电气工程
- 3,829 篇 控制科学与工程
- 2,908 篇 软件工程
- 1,924 篇 信息与通信工程
- 1,554 篇 光学工程
- 1,406 篇 机械工程
- 997 篇 仪器科学与技术
- 583 篇 电子科学与技术（可...
- 549 篇 生物医学工程（可授...
- 433 篇 生物工程
- 232 篇 材料科学与工程（可...
- 195 篇 交通运输工程
- 163 篇 安全科学与工程
- 154 篇 化学工程与技术
- 139 篇 力学（可授工学、理...
- 115 篇 建筑学
- 110 篇 土木工程
- 104 篇 航空宇航科学与技...
3,400 篇 理学
- 2,548 篇 物理学
- 805 篇 数学
- 486 篇 生物学
- 295 篇 系统科学
- 209 篇 统计学（可授理学、...
- 134 篇 化学
1,654 篇 医学
- 1,577 篇 临床医学
- 185 篇 基础医学(可授医学...
761 篇 管理学
- 582 篇 管理科学与工程(可...
- 190 篇 图书情报与档案管...
- 120 篇 工商管理
107 篇 农学
78 篇 法学
43 篇 经济学
42 篇 教育学
39 篇 艺术学
37 篇 军事学
18 篇 文学

主题

2,736 篇 computer vision
1,685 篇 cameras
1,489 篇 signal processin...
1,441 篇 robot vision sys...
1,355 篇 image processing
1,173 篇 robot sensing sy...
912 篇 signal processin...
874 篇 mobile robots
839 篇 feature extracti...
769 篇 machine vision
549 篇 image segmentati...
504 篇 object detection
440 篇 visualization
424 篇 deep learning
409 篇 robustness
393 篇 estimation
367 篇 stereo vision
358 篇 navigation
343 篇 training
319 篇 robot kinematics

机构

83 篇 centre for visio...
63 篇 xi an jiao tong ...
54 篇 centre for visio...
37 篇 school of electr...
36 篇 centre for visio...
29 篇 carnegie mellon ...
28 篇 chinese acad sci...
27 篇 shanghai jiao to...
27 篇 center for machi...
27 篇 university of ch...
23 篇 centre for visio...
23 篇 harbin inst tech...
21 篇 univ chinese aca...
21 篇 nanyang technol ...
17 篇 centre for visio...
16 篇 university of sc...
16 篇 tsinghua univers...
13 篇 chinese acad sci...
13 篇 univ sci & techn...
13 篇 chinese univ hon...

作者

52 篇 j. kittler
40 篇 josef kittler
28 篇 nakadai kazuhiro
19 篇 anil fernando
18 篇 wang wei
15 篇 chen chen
14 篇 yang yang
14 篇 nascimento jacin...
13 篇 jing zhang
13 篇 liu yang
13 篇 sun fuchun
12 篇 sun lining
12 篇 hansung kim
11 篇 zhang lei
11 篇 bartolozzi chiar...
11 篇 hong liu
10 篇 wang lei
10 篇 li yang
10 篇 aguiar pedro m. ...
10 篇 qiuqiang kong

语言

17,927 篇 英文
86 篇 中文
84 篇 其他
12 篇 土耳其文
3 篇 俄文
2 篇 西班牙文

检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18111 条记录，以下是381-390 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A Literature Review of Machine Learning Techniques for Dance Recognition and robotic vision

A Literature Review of Machine Learning Techniques for Dance...

引用

robotics and Technologies for Industrial Automation (robotHIA), IEEE international conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots robot sensing systems Real-time systems robots Long short term memory

来源：评论

学校读者我要写书评

暂无评论

iSense: A Wearable Solution to Enhance Accessibility for Visually Impaired People 25

iSense: A Wearable Solution to Enhance Accessibility for Vis...

引用

Proceedings of the 2025 ACM/IEEE international conference on Human-robot Interaction

作者： Saeed Vahdani Amirhossein Nazari Ali Bokaeian Saleh Ebrahimian Omid Salehinia Saleh Farsi Elham Armin Maryam Saeedi Fard Mohammad Sadegh Ahmadi Amirmahdi Zarif Shahsavan Nejad Seyyed Ahmad Nezami Mohammad Mahdi Arvili Ali Vahdani Ahad Harati Ali Esmaeili iSense Project Ferdowsi University of Mashhad Mashhad Iran iSense Project & Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran iSense Project & Department of Mechanical Engineering Ferdowsi University of Mashhad Mashhad Iran

vision impairment significantly impacts independence and quality of life. This paper presents iSense, an advanced wearable assistive device for visually impaired users, which integrates RealSense RGB-D cameras, haptic-auditory feedback, and a high-performance processing platform. Key features include obstacle detection, spatial navigation, object recognition, and task assistance in daily environments. Experimental results highlight the potential of iSense to enhance mobility and independence, exemplifying the role of assistive robotics in promoting inclusivity.

关键词： human-robot interaction

来源：评论

学校读者我要写书评

暂无评论

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shitong Fan Feiyang Xiao Wenbo Wang Shuhan Qi Qiaoxi Zhu Wenwu Wang Jian Guan Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge’s Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10.

关键词： Location awareness Smart cities signal processing Feature extraction Acoustics Data models Microphone arrays Speech processing Monitoring Faces

来源：评论

学校读者我要写书评

暂无评论

Efficient Fine-tuning Strategies for Enhancing Face Recognition Performance in Challenging Scenarios

Efficient Fine-tuning Strategies for Enhancing Face Recognit...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yin Lin Ziyang Wu Qidong Huang Xinran Liu Baocai Yin Jinshui Hu Bing Yin Zengfu Wang University of Science and Technology of China China iFLYTEK Research China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Face recognition plays a crucial role in human life, prompting numerous excellent research efforts. However, face recognition in real-world applications presents various scenarios such as occluded, overexposed and near-infrared face recognition. Due to domain discrepancy and a lack of large-scale training data, effectively transferring pre-trained face recognition models to these scenarios has become a challenge. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have shown great potential in natural language processing tasks, but their effectiveness in computer vision tasks, especially in face recognition tasks, remains under-explored. In this paper, we propose a Data-Parameter-Efficient Fine-Tuning (DPEFT) approach for the face recognition tasks, encompassing two kinds of fine-tuning strategies. With these strategies, the DPEFT method requires only an additional 2.7% learnable parameters and 20% of the training data during the training phase to achieve competitive results. Moreover, by further integrating the concept of structural re-parameterization, our approach maintains the same model architecture and parameters as the pre-trained model during inference. Extensive experimental results on both holistic and occluded face datasets demonstrate that our approach achieves performance comparable to or better than the fully fine-tuning methods, and significantly lower training costs. Our DPEFT enables the pre-trained face recognition model to adapt efficiently and effectively to a variety of scenarios, indicating its potential in practical applications.

关键词： Training Adaptation models Costs Face recognition Training data Speech recognition signal processing Natural language processing Data models Speech processing

来源：评论

学校读者我要写书评

暂无评论

Enhancing vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues

Enhancing Vision-Language Tracking by Effectively Converting...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xiaokun Feng Dailing Zhang Shiyu Hu Xuchen Li Meiqi Wu Jing Zhang Xiaotang Chen Kaiqi Huang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current datasets typically contain much more image data than text, limiting the ability of VLT methods to align the two modalities effectively. To address this imbalance, we propose a novel plug-and-play method named CTVLT that leverages the strong text-image alignment capabilities of foundation grounding models. CTVLT converts textual cues into interpretable visual heatmaps, which are easier for trackers to process. Specifically, we design a textual cue mapping module that transforms textual cues into target distribution heatmaps, visually representing the location described by the text. Additionally, the heatmap guidance module fuses these heatmaps with the search image to guide tracking more effectively. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our approach, achieving state-of-the-art performance and validating the utility of our method for enhanced VLT.

关键词： Heating systems Visualization Target tracking Grounding Video sequences Training data Transforms Benchmark testing Numerical models Speech processing

来源：评论

学校读者我要写书评

暂无评论

PNA - Net: Leveraging Deep Convolutional Neural Network for Enhanced Pneumonia Detection from Chest X-Ray

PNA - Net: Leveraging Deep Convolutional Neural Network for ...

引用

international conference on robotics,Electrical and signal processing Techniques (ICREST)

作者： Amit Roy Md Mahfujur Rahman Dewan Ziaul Karim Department of CSE Brac University Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Pneumonia is a type of respiratory infection, and it is marked as one of the most deadly diseases in many countries. Sometimes moving to a medical center is difficult, b ut early diagnosis is very important. To encounter the problem, computer vision-based models can be a good solution. In this paper, we propose a custom CNN architecture named “PNA-Net” to detect pneumonia using chest X-ray images. We use 5856 images and split them into train and test sections with 5232 and 624, respectively. Our model attained 97.24% training accuracy and 97.90% validation accuracy. We made a comparison with some pretrained models; for instance, Resnet50, InceptionV3, VGG19, MobileNet, DenseNet121, VGG16, and Ensemble model, and their accuracies were 90.06%, 94.07%, 95.92%, 96.56%, 96.12%, 94.98%, and 94.84%, respectively. The overall study showed that our custom CNN model provided better accuracy than the previous works for the used dataset because of its tailored architecture with flexible h yperparameter tuning, outperformed all pretrained models, and can offer significant a ssistance in making clinical decisions and enhancing patient care.

关键词： Training Pneumonia Accuracy Computational modeling Computer architecture signal processing Convolutional neural networks X-ray imaging Tuning Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Make Unseen Clear: Occluder Removal for Complete 3D Pedestrian Detection

Make Unseen Clear: Occluder Removal for Complete 3D Pedestri...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yan Luo Zefeng Qian Chongyang Zhang Yi Wang Xiaolei Hu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China E-Surfing Vision Technology Co. Ltd Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In autonomous driving, the ability to detect pedestrians accurately is crucial for safety. Some detectors, however, often struggle with occlusions, where pedestrians partially hidden behind objects appear incomplete and are harder to be identified accurately. To alleviate this issue, we introduce Clear3D, which make previous partially unseen pedestrians visible by effectively removing occluders to enhance 3D pedestrian detection. The first essential step in Clear3D is to accurately identify occluded regions. This is achieved by calculating the intensity variation along the LiDAR ray direction, where regions with small changes are classified as occluded. After identifying the occluded areas, they are transformed as masks and fed into the inpainting module, reconstructing the originally unseen parts. Above process, encompassing both the perception and reconstruction of occluded regions, is termed occluder removal. Finally, by fusing the recovered occlusion-free image features with LiDAR features, Clear3D generates more complete representation, enhancing the performance of downstream 3D classification and localization tasks. Extensive evaluations show that Clear3D achieves state-of-the-art accuracy across various challenging settings on KITTI, particularly in heavy occlusion environments.

关键词： Pedestrians Three-dimensional displays Laser radar Accuracy signal processing Safety Object recognition Speech processing Image reconstruction Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hao Yu Haoyu Chen Guoying Zhao Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: ***/CV-AC/BIB-FAS.

关键词： Training Representation learning Technological innovation Correlation Codes signal processing Data mining Speech processing Faces Optimization

来源：评论

学校读者我要写书评

暂无评论

Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments

Audio-Driven Reinforcement Learning for Head-Orientation in ...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wessel Ledder Yuzhen Qin Kiki van der Heijden Radboud University Nijmegen Netherlands Donders Institute Radboud University Nijmegen Netherlands Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York United States

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Although deep reinforcement learning (DRL) approaches in audio signal processing have seen substantial progress in recent years, fully audio-driven DRL for tasks such as navigation, gaze control and head-orientation control have received little attention. Yet, such audio-driven DRL approaches are highly relevant for the development of fully autonomous audio-based agents as they can be seamlessly merged with other audio-driven (DRL) approaches such as automatic speech recognition and emotion recognition. Therefore, we propose an end-to-end, audio-driven DRL framework in which we utilize deep Q-learning to develop an autonomous agent that orients towards a talker in the acoustic environment based on stereo speech recordings. Our results show that the agent learned to perform the task in a range of naturalistic acoustic environments with varying degrees of reverberation. Quantifying the degree of generalization of the proposed DRL approach across acoustic environments revealed that policies learned by an agent trained on medium or high reverb environments generalized to low reverb environments, but policies learned by an agent trained on anechoic or low reverb environments did not generalize to medium or high reverb environments. Taken together, this study demonstrates the potential of fully audio-driven DRL for tasks such as head-orientation control. Furthermore, our findings highlight the need for training strategies that enable robust generalization across acoustic environments in order to develop real-world audio-driven DRL applications 1 .

关键词： Training Q-learning Navigation Process control Human-robot interaction Autonomous agents Machine listening Recording Reverberation Speech processing

来源：评论

学校读者我要写书评

暂无评论

Classification of Eye-Tracking Data Based on Spatiotemporal Attention Encoding

Classification of Eye-Tracking Data Based on Spatiotemporal ...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiaju He Chen Xia Kuan Li Tian Zhang Beijing University of Chemical Technology Northwestern Polytechnical University The Affiliated Hospital of Northwest University Xi’an Jiaotong University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Eye movement classification can decode cognitive processes, offering valuable insights for a wide range of applications. However, existing eye movement classification models primarily focus on static fixation-based features and often neglect the encoding of spatiotemporal eye movement features, which are crucial for accurately reconstructing visual attention. To address this limitation, we propose a spatiotemporal attention encoding (STAE) model that jointly captures both spatial and temporal features for eye-tracking classification. First, we utilize a vision Transformer (ViT) to extract spatial features from fixations by taking global competition into consideration. We then introduce a global weighting Gated Recurrent Unit (GRU) model to capture temporal correlations from the feature sequence. Specifically, we propose a hidden-state-based weighting to fuse the influence of different fixations on the current fixation. In the experiment, we evaluated our model on three tasks: autism spectrum disorder (ASD) identification, visual task classification, and age classification. Experiential results across three databases demonstrate that our model outperforms existing methods and shows strong adaptability across various eye movement classification tasks. The code is available at https://***/HectorTo/spatiotemporal-attention-encoding-STAE-.

关键词： Adaptation models Visualization Fuses Gaze tracking signal processing Feature extraction Transformers Encoding Spatiotemporal phenomena Speech processing

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 35 36 37 38 39 40 41 42 43 44 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：