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

8,902 篇 会议
43 篇 期刊文献
18 册 图书

馆藏范围

8,962 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,560 篇 工学
- 4,020 篇 计算机科学与技术...
- 2,178 篇 软件工程
- 1,241 篇 光学工程
- 555 篇 控制科学与工程
- 431 篇 信息与通信工程
- 430 篇 机械工程
- 294 篇 电气工程
- 287 篇 仪器科学与技术
- 179 篇 生物工程
- 159 篇 生物医学工程（可授...
- 119 篇 电子科学与技术（可...
- 61 篇 安全科学与工程
- 58 篇 建筑学
- 58 篇 化学工程与技术
- 52 篇 土木工程
- 49 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,065 篇 理学
- 1,382 篇 物理学
- 1,198 篇 数学
- 420 篇 统计学（可授理学、...
- 238 篇 生物学
- 54 篇 化学
- 36 篇 系统科学
263 篇 管理学
- 180 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 47 篇 工商管理
223 篇 医学
- 222 篇 临床医学
- 39 篇 基础医学(可授医学...
205 篇 艺术学
- 205 篇 设计学（可授艺术学...
45 篇 法学
- 43 篇 社会学
21 篇 农学
14 篇 教育学
9 篇 经济学
6 篇 军事学

主题

3,413 篇 computer vision
1,216 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
618 篇 layout
598 篇 training
548 篇 shape
518 篇 robustness
451 篇 feature extracti...
448 篇 humans
445 篇 face recognition
405 篇 computational mo...
402 篇 object detection
365 篇 visualization
356 篇 computer archite...
336 篇 application soft...
304 篇 lighting
258 篇 image reconstruc...

机构

41 篇 microsoft resear...
30 篇 department of co...
25 篇 department of co...
23 篇 institute for co...
22 篇 department of co...
22 篇 school of comput...
20 篇 university of sc...
20 篇 swiss fed inst t...
19 篇 tsinghua univers...
19 篇 institute of com...
18 篇 swiss fed inst t...
17 篇 the robotics ins...
17 篇 carnegie mellon ...
17 篇 computer vision ...
17 篇 department of co...
16 篇 institute of inf...
16 篇 school of comput...
15 篇 school of comput...
15 篇 carnegie mellon ...
14 篇 national laborat...

作者

57 篇 timofte radu
25 篇 huang thomas s.
24 篇 van gool luc
23 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 t. kanade
21 篇 jain anil k.
20 篇 luc van gool
19 篇 t.s. huang
18 篇 xiaoou tang
18 篇 murino vittorio
18 篇 horst bischof
17 篇 a.k. jain
17 篇 t. darrell
16 篇 g. healey
16 篇 bowyer kevin w.
16 篇 bischof horst
15 篇 m.j. black
15 篇 li stan z.
15 篇 m. shah

语言

8,932 篇 英文
22 篇 其他
8 篇 中文
1 篇 土耳其文

检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8963 条记录，以下是1421-1430 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：

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional pattern for Perceptual Image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 ieee.

关键词： Hamming distance

来源：评论

学校读者我要写书评

暂无评论

Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexander Wong Yifan Wu Saad Abbasi Saeejith Nair Yuhao Chen Mohammad Javad Shafiee Vision and Image Processing Research Group University of Waterloo Waterloo Artificial Intelligence Institute Waterloo ON DarwinAI Corp. Waterloo ON

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MultiPanoWise: holistic deep architecture for multi-task dense prediction from a single panoramic image

MultiPanoWise: holistic deep architecture for multi-task den...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Uzair Shah Muhammad Tukur Mahmood Alzubaidi Giovanni Pintore Enrico Gobbetti Mowafa Househ Jens Schneider Marco Agus ICT Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Visual and Data-Intensive Computing CRS4 Italy National Research Center in HPC Big Data and Quantum Computing Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a novel holistic deep-learning approach for multi-task learning from a single indoor panoramic image. Our framework, named MultiPanoWise, extends vision transformers to jointly infer multiple pixel-wise signals, such as depth, normals, and semantic segmentation, as well as signals from intrinsic decomposition, such as reflectance and shading. Our solution leverages a specific architecture combining a transformer-based encoder-decoder with multiple heads, by introducing, in particular, a novel context adjustment approach, to enforce knowledge distillation between the various signals. Moreover, at training time we introduce a hybrid loss scalarization method based on an augmented Chebychev/hypervolume scheme. We illustrate the capabilities of the proposed architecture on public-domain synthetic and real-world datasets. We demonstrate performance improvements with respect to the most recent methods specifically designed for single tasks, like, for example, individual depth estimation or semantic segmentation. To our knowledge, this is the first architecture capable of achieving state-of-the-art performance on the joint extraction of heterogeneous signals from single indoor omnidirectional images.

关键词： Training Reflectivity computer vision Semantic segmentation Estimation computer architecture Multitasking

来源：评论

学校读者我要写书评

暂无评论

Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Joint Physical-Digital Facial Attack Detection Via Simulatin...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xianhua He Dashuang Liang Song Yang Zhanlong Hao Hui Ma Binjie Mao Xi Li Yao Wang Pengfei Yan Ajian Liu Vision AI Department Meituan M.U.S.T MAIS CASIA China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks, implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model, we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation, which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset, respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@CVPR2024. Our final submission obtains 3.75% APCER, 0.93% BPCER, and 2.34% ACER, respectively. Our code is available at https://***/Xianhua-He/cvpr2024-face-anti-spoofing-challenge.

关键词： Adaptation models computer vision Protocols Codes Face recognition conferences Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT

Mitigating Challenges of the Space Environment for Onboard A...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Miguel Ortiz Del Castillo Jonathan Morgan Jack McRobbie Clint Therakam Zaher Joukhadar Robert Mearns Simon Barraclough Richard Sinnott Andrew Woods Chris Bayliss Kris Ehinger Ben Rubinstein James Bailey Airlie Chapman Michele Trenti The University of Melbourne

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control. Here, we present the hardware and software design of an onboard AI subsystem hosted on SpIRIT. The system is optimised for on-board computer vision experiments based on visible light and long wave infrared cameras. This paper highlights the key design choices made to maximise the robustness of the system in harsh space conditions, and their motivation relative to key mission requirements, such as limited compute resources, resilience to cosmic radiation, extreme temperature variations, distribution shifts, and very low transmission bandwidths. The payload, called Loris, consists of six visible light cameras, three infrared cameras, a camera control board and a Graphics Processing Unit (GPU) system-on-module. Loris enables the execution of AI models with on-orbit fine-tuning as well as a next-generation image compression algorithm, including progressive coding. This innovative approach not only enhances the data processing capabilities of nanosatellites but also lays the groundwork for broader applications to remote sensing from space.

关键词： computer vision Temperature distribution Image coding Space missions Small satellites Graphics processing units Aerospace electronics

来源：评论

学校读者我要写书评

暂无评论

QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Piotr Kluska Adrián Castelló Florian Scheidegger A. Cristiano I. Malossi Enrique S. Quintana-Ortí IBM Research Europe Universitat Politècnica de València Universitat Politècnica de València IBM Research Europe

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers –linear layer and attention– on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 ± 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： computer vision Image segmentation Quantization (signal) Computational modeling Graphics processing units Prototypes Transformers

来源：评论

学校读者我要写书评

暂无评论

Swift Parameter-free Attention Network for Efficient Super-Resolution

Swift Parameter-free Attention Network for Efficient Super-R...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zuo Georgia Institute of Technology Xiaomi Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional attention mechanisms have significantly improved SISR performance but often result in complex network structures and large number of parameters, leading to slow inference speed and large model size. To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality. SPAN employs a novel parameter-free attention mechanism, which leverages symmetric activation functions and residual connections to enhance high-contribution information and suppress redundant information. Our theoretical analysis demonstrates the effectiveness of this design in achieving the attention mechanism’s purpose. We evaluate SPAN on multiple benchmarks, showing that it outperforms existing efficient super-resolution models in terms of both image quality and inference speed, achieving a significant quality-speed trade-off. This makes SPAN highly suitable for real-world applications, particularly in resource-constrained scenarios. Notably, we won the first place both in the overall performance track and runtime track of the NTIRE 2024 efficient super-resolution challenge. Our code and models are made publicly available at https://***/hongyuanyu/span.

关键词： Image quality Performance evaluation computer vision Attention mechanisms Runtime Computational modeling Superresolution

来源：评论

学校读者我要写书评

暂无评论

Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Keyao Wang Mouxiao Huang Guosheng Zhang Haixiao Yue Gang Zhang Yu Qiao Department of Computer Vision Technology (VIS) Baidu Inc. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model’s ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NORPPA: NOvel Ringed Seal Re-Identification by Pelage pattern Aggregation

NORPPA: NOvel Ringed Seal Re-Identification by Pelage Patter...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Ekaterina Nepovinnykh Tuomas Eerola Heikki Kälviäinen Ilia Chelak Department of Computational Engineering School of Engineering Sciences Computer Vision and Pattern Recognition Laboratory (CVPRL) Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Computer Science Faculty of Science University of Helsinki Helsinki Finland

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution

Multi-Scale Feature Fusion using Channel Transformers for Gu...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Raghunath Sai Puttagunta Birendra Kathariya Zhu Li George York University of Missouri-Kansas City US Air Force Academy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low- light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution. We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal Image Super-Resolution (GTISR).GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs. At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale *** overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs. MSFFCT addresses these shortcomings by enabling efficient processing of global information and offering superior scalability. MSFFCT achieved state-of-the-art results on the ×8 and × 16 GTISR tasks of the 2024 Perception Beyond Visual Spectrum (PBVS) challenge, winning 2 nd place in both tasks and demonstrating its effectiveness in real-world scenarios.

关键词： computer vision Visualization Limiting Superresolution Imaging Transformer cores Transformers

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 << < 139 140 141 142 143 144 145 146 147 148 > >>

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：