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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
- 2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,319 篇 英文
1 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是221-230 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：

Super-Resolution based Video Coding Scheme

Super-Resolution based Video Coding Scheme

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cho, Hyun Min Choi, Kiho Gacheon Univ Sch Comp 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present a super-resolution-based video coding scheme that compresses video data by combining traditional hybrid video coding and Convolutional neural network-based video coding. During video encoding, downsampling reduces the resolution of an original video in both horizontal and vertical directions to reduce original video data, and Convolutional neural networkbased super-resolution is employed after the decoding process to recover the resolution of the reconstructed video during upsampling. For core encoding and decoding processes, the latest video coding standard (i.e., VVC/H.266) is conducted. The experimental results show that the proposed method can provide efficient coding performance while maintaining good visual quality.

关键词： Video coding Visualization Image coding Superresolution Encoding Decoding pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Image Reconstruction from Neuromorphic Event Cameras using Laplacian-Prediction and Poisson Integration with Spiking and Artificial Neural Networks

Image Reconstruction from Neuromorphic Event Cameras using L...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Duwek, Hadar Cohen Shalumov, Albert Tsur, Elishai Ezra Open Univ Israel Neurobiomorph Engn Lab NBEL Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665448994

Event cameras are robust neuromorphic visual sensors, which communicate transients in luminance as events. Current paradigm for image reconstruction from event data relies on direct optimization of artificial Convolutional Neural Networks (CNNs). Here we proposed a two-phase neural network, which comprises a CNN, optimized for Laplacian prediction followed by a Spiking Neural Network (SNN) optimized for Poisson integration. By introducing Laplacian prediction into the pipeline, we provide image reconstruction with a network comprising only 200 parameters. We converted the CNN to SNN, providing a full neuromorphic implementation. We further optimized the network with Mish activation and a novel convoluted CNN design, proposing a hybrid of spiking and artificial neural network with < 100 parameters. Models were evaluated on both N-MNIST and N-Caltech101 datasets.

关键词： Visualization Laplace equations Neuromorphics Pipelines Cameras Sensors pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

来源：评论

学校读者我要写书评

暂无评论

CompConv: A Compact Convolution Module for Efficient Feature Learning

CompConv: A Compact Convolution Module for Efficient Feature...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Chen Xu, Yinghao Shen, Yujun Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing approaches either compress well-trained large-scale models or learn lightweight models with carefully designed network structures. In this work, we make a close study of the convolution operator, which is the basic unit used in CNNs, to reduce its computing load. In particular, we propose a compact convolution module, called CompConv, to facilitate efficient feature learning. With the divide-and-conquer strategy, CompConv is able to save a great many computations as well as parameters to produce a certain dimensional feature map. Furthermore, CompConv discreetly integrates the input features into the outputs to efficiently inherit the input information. More importantly, the novel CompConv is a plug-and-play module that can be directly applied to modern CNN structures to replace the vanilla convolution layers without further effort. Extensive experimental results suggest that CompConv can adequately compress the benchmark CNN structures yet barely sacrifice the performance, surpassing other competitors.

关键词： computer vision Convolution Computational modeling conferences Benchmark testing pattern recognition Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym: Full-Featured PyTorch Library for Continual Learning

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Mirzadeh, Seyed Iman Ghasemzadeh, Hassan Washington State Univ Pullman WA 99164 USA

ISBN: (纸本)9781665448994

Continual learning (CL) has become one of the most active research venues within the artificial intelligence community in recent years. Given the significant amount of attention paid to continual learning, the need for a library that facilitates both research and development in this field is more visible than ever. However, CL algorithms' codes are currently scattered over isolated repositories written with different frameworks, making it difficult for researchers and practitioners to work with various CL algorithms and benchmarks using the same interface. In this paper, we introduce CL-Gym, a full-featured continual learning library that overcomes this challenge and accelerates the research and development. In addition to the necessary infrastructure for running end-to-end continual learning experiments, CL-Gym includes benchmarks for various CL scenarios and several state-of-the-art CL algorithms. In this paper, we present the architecture, design philosophies, and technical details behind CL-Gym (1).

关键词： computer vision Philosophical considerations conferences computer architecture Learning (artificial intelligence) Benchmark testing Libraries

来源：评论

学校读者我要写书评

暂无评论

Active vision Dataset Benchmark 31

Active Vision Dataset Benchmark

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ammirato, Phil Berg, Alexander C. Kosecka, Jana Univ N Carolina Chapel Hill NC 27514 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.

关键词： Task analysis Training Visualization Robots Benchmark testing Navigation

来源：评论

学校读者我要写书评

暂无评论

A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

A Universal Encoder Rate Distortion Optimization Framework f...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Jing Li, Bin Li, Jiahao Xiong, Ruiqin Lu, Yan Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665448994

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.

关键词： computer vision Image coding Codecs conferences Bit rate Rate-distortion Optimization methods

来源：评论

学校读者我要写书评

暂无评论

CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

CNN-based morphological decomposition of X-ray images for de...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Madmad, Tahani Delinte, Nicolas De Vleeschouwer, Christophe UCLouvain ICTEAM Louvain La Neuve Belgium

ISBN: (纸本)9781665448994

This paper introduces a new learning based framework for X-ray images that relies on a morphological decomposition of the signal into two main components, separating images into local textures and piecewise smooth (cartoon) parts. The piecewise smooth component corresponds to the spatial variation of the average density of the objects, whereas the local texture component presents the inspected objects singularities. Our method builds on two convolutional neural network (CNN) branches to decompose an input image into its two morphological components. This CNN is trained with synthetic data, generated by randomly picking piecewise smooth and singular patterns in a parametric dictionary and enforcing the sum of the CNN branches to approximate the identity mapping. We demonstrate the relevance of the decomposition by enhancing the local textures component compared to the piecewise smooth one. Those enhanced images compare favorably to the ones obtained with existing works destined to visualize High Dynamic Range (HDR) images such as tone-mapping algorithms.

关键词： computer vision Dictionaries Heuristic algorithms conferences Data visualization Dynamic range Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Pose Tutor: An Explainable System for Pose Correction in the Wild

Pose Tutor: An Explainable System for Pose Correction in the...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dittakavi, Bhat Bavikadi, Divyagna Desai, Sai Vikas Chakraborty, Soumi Reddy, Nishant Balasubramanian, Vineeth N. Callepalli, Bharathi Sharma, Ayon IIT Hyderabad Hyderabad Telangana India Variance AI Hyderabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Under the new norm of working from home, demand for fitness from home is on the rise. Different exercise forms solve different fitness needs for different people. Yoga gives flexibility and relieves stress. Pilates strengthens the muscles. Kung Fu brings balance. It is not feasible for everyone to hire a personal trainer. In this paper, we develop Pose Tutor, an AI-based explainable pose recognition and correction system. Pose Tutor combines vision and pose skeleton models in a novel coarse-to-fine framework to obtain pose class predictions. An angle-likelihood mechanism is used to explain which human joints maximally caused the pose class predictions and also correct any wrongly formed joints. Even without keypoint level training, Pose Tutor shows promising results on Yoga-82, Pilates-32, and Kungfu-7 datasets. Additionally, user studies conducted with multiple domain experts validate the explanations provided by our framework.

关键词： Training computer vision conferences Predictive models Muscles Skeleton pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Prabhune, Omkar Chen, Tianen Kim, Younghyun Purdue Univ W Lafayette IN 47907 USA Univ Wisconsin Madison WI 53706 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798350365474

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication overhead. In this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a contentaware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： AI on edge computation offloading content-aware compression content-aware offloading deep learning edge computing edge ML efficient deep learning efficient ML embedded vision face identification low-latency deep learning low-power ML ml system tinyML

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332页 << < 19 20 21 22 23 24 25 26 27 28 > >>

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：