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

3,310 篇 会议
3 篇 期刊文献

馆藏范围

3,313 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,936 篇 工学
- 1,843 篇 计算机科学与技术...
- 201 篇 软件工程
- 147 篇 机械工程
- 134 篇 光学工程
- 41 篇 生物工程
- 28 篇 信息与通信工程
- 18 篇 电气工程
- 13 篇 控制科学与工程
- 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
360 篇 医学
- 359 篇 临床医学
- 3 篇 基础医学(可授医学...
178 篇 理学
- 137 篇 物理学
- 42 篇 生物学
- 30 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
- 3 篇 系统科学
14 篇 管理学
- 7 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 农学
1 篇 经济学

主题

1,738 篇 computer vision
900 篇 training
802 篇 conferences
643 篇 pattern recognit...
482 篇 computational mo...
431 篇 computer archite...
430 篇 task analysis
426 篇 visualization
350 篇 feature extracti...
317 篇 semantics
308 篇 three-dimensiona...
243 篇 neural networks
227 篇 benchmark testin...
222 篇 cameras
183 篇 image segmentati...
177 篇 estimation
168 篇 deep learning
156 篇 measurement
154 篇 object detection
151 篇 data models

机构

40 篇 univ sci & techn...
31 篇 peng cheng lab p...
30 篇 sensetime res pe...
29 篇 swiss fed inst t...
27 篇 university of sc...
27 篇 zhejiang univ pe...
26 篇 univ chinese aca...
26 篇 swiss fed inst t...
26 篇 university of ch...
25 篇 sun yat sen univ...
24 篇 univ chinese aca...
24 篇 nanyang technol ...
23 篇 tsinghua univ pe...
22 篇 peng cheng labor...
21 篇 korea adv inst s...
20 篇 shanghai ai lab ...
19 篇 chinese univ hon...
19 篇 yonsei univ
18 篇 peking univ peop...
18 篇 tsinghua univers...

作者

65 篇 timofte radu
21 篇 loy chen change
18 篇 van gool luc
16 篇 zha zheng-jun
16 篇 radu timofte
14 篇 sun jian
12 篇 fan haoqiang
12 篇 liu shuai
11 篇 zheng wei-shi
11 篇 zheng-jun zha
11 篇 liu shuaicheng
11 篇 qi tian
11 篇 luc van gool
11 篇 pan jinshan
11 篇 marcos v. conde
11 篇 chen wei-ting
11 篇 li chenghua
11 篇 liu jing
11 篇 van de weijer jo...
11 篇 lei lei

语言

3,311 篇 英文
2 篇 其他

检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020"

共 3313 条记录，以下是871-880 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：

Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andrea Panzino Simone Maurizio La Cava Giulia Orrù Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the possibility of automatically verifying an individual’s identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： computer vision Face recognition conferences Employment Process control Detectors Market research

来源：评论

学校读者我要写书评

暂无评论

LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 ieee.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Cross-Domain Multi-task Learning for Object Detection and Sa...

引用

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

作者： Khattar, Apoorv Hegde, Srinidhi Hebbalaguppe, Ramya TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665448994

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.

关键词： Training computer vision Neural networks Estimation Object detection computer architecture pattern recognition

来源：评论

学校读者我要写书评

暂无评论

A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

A Tale of Two CILs: The Connections between Class Incrementa...

引用

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

作者： He, Chen Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665448994

Catastrophic forgetting, the main challenge of Class Incremental Learning, is closely related to the classifier's bias due to imbalanced data, and most researchers resort to empirical techniques to remove the bias. Such anti-bias tricks share many ideas with the field of Class Imbalanced Learning, which encourages us to reflect on why these tricks work, and how we can design more principled solutions from a different perspective. In this paper, we comprehensively analyze the connections and seek possible collaborations between these two fields, i.e. Class Incremental Learning and Class Imbalanced Learning. Specifically, we first provide a panoramic view of recent bias correction tricks from the perspective of handling class imbalance. Then, we show that an adapted post-scaling technique which originates from Class Imbalanced Learning is on par with or even outperforms SOTA Class Incremental Learning method. Visualization via violin plots and polar charts further sheds light on how SOTA methods address the class imbalance problem from a more intuitive geometric perspective. These findings may encourage further infiltration between the two closely connected fields, but also raise concerns about whether it is correct that Class Incremental Learning degenerates into a class imbalance problem.

关键词： Learning systems computer vision conferences Collaboration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

DAMSL: Domain Agnostic Meta Score-based Learning

DAMSL: Domain Agnostic Meta Score-based Learning

引用

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

作者： Cai, John Cai, Bill Mei, Shen Sheng Princeton Univ Princeton NJ 08544 USA MIT Cambridge MA 02139 USA Pensees Pte Ltd Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL), a novel, versatile and highly effective solution that delivers significant out-performance over state-of-the-art methods for cross-domain few-shot learning. We identify key problems in previous meta-learning methods over-fitting to the source domain, and previous transfer-learning methods under-utilizing the structure of the support set. The core idea behind our method is that instead of directly using the scores from a fine-tuned feature encoder, we use these scores to create input coordinates for a domain agnostic metric space. A graph neural network is applied to learn an embedding and relation function over these coordinates to process all information contained in the score distribution of the support set. We test our model on both established CD-FSL benchmarks and new domains and show that our method overcomes the limitations of previous meta-learning and transfer-learning methods to deliver substantial improvements in accuracy across both smaller and larger domain shifts.

关键词： computer vision conferences Computational modeling Performance gain Benchmark testing Extraterrestrial measurements Boosting

来源：评论

学校读者我要写书评

暂无评论

ILCOC: An Incremental Learning Framework based on Contrastive One-class Classifiers

ILCOC: An Incremental Learning Framework based on Contrastiv...

引用

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

作者： Sun, Wenju Zhang, Jing Wang, Danyu Geng, Yangli-Ao Li, Qingyong Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (纸本)9781665448994

In the class incremental learning, the number of classes to be handled dynamically raises with the number of considered tasks. The main challenge of this learning schema is catastrophic forgetting, that is the performance degradation on old tasks after learning new tasks. Existing incremental learning algorithms generally choose to train a multi-class classifier (e.g. softmax classifier), which learns a decision boundary to divide the feature space into several parts. Therefore, when new data arrive, the learned boundary will be updated and thus may cause forgetting. Compared with multi-class classifiers, a one-class classifier focuses on characterizing the distribution of a single class. As a result, the decision boundary learned for each category is tighter and does not change during learning new tasks. Inspired by this characteristic of one-class classifier, we propose a novel Incremental Learning framework based on Contrastive One-class Classifiers (ILCOC) to avoid catastrophic forgetting. Specifically, we train a specific one-class classifier for each category and parallelly use them to achieve incremental multi-class recognition. Besides, we design a scale-boundary loss, a classifier-contrastive loss and a negative-suppression loss to strengthen the comparability of classifiers outputs and the discrimination ability of each one-class classifier. We evaluate ILCOC on MNIST, CIFAR-10 and Tiny-ImageNet datasets, and the experimental results show that ILCOC achieves state-of-the-art performance.(1)

关键词： Degradation computer vision Heuristic algorithms conferences Computational modeling pattern recognition Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Table Tennis Stroke recognition Using Two-Dimensional Human Pose Estimation

Table Tennis Stroke Recognition Using Two-Dimensional Human ...

引用

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

作者： Kulkarni, Kaustubh Milind Shenoy, Sucheth

ISBN: (纸本)9781665448994

We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player's strokes, which is extremely insightful for performance improvement.

关键词： Training Deep learning computer vision Computational modeling Pose estimation Neural networks Stroke (medical condition)

来源：评论

学校读者我要写书评

暂无评论

Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay

Dual-Teacher Class-Incremental Learning With Data-Free Gener...

引用

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

作者： Choi, Yoojin El-Khamy, Mostafa Lee, Jungwon Samsung Semicond Inc SoC R&D San Diego CA 92121 USA Samsung Elect Syst LSI Suwon South Korea

ISBN: (纸本)9781665448994

This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on the pre-trained classification model from the past, so we curtail the cost of sharing pre-trained generative models. Second, we introduce dual-teacher information distillation (DT-ID) for knowledge distillation from two teachers to one student. In CIL, we use DT-ID to learn new classes incrementally based on the pre-trained model for old classes and another model (pre-)trained on the new data for new classes. We implemented the proposed schemes on top of one of the state-of-the-art CIL methods and showed the performance improvement on CIFAR-100 and ImageNet datasets.

关键词： Data privacy computer vision conferences Training data Data models pattern recognition Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

Transformer Tracking with Cyclic Shifting Window Attention

Transformer Tracking with Cyclic Shifting Window Attention

引用

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

作者： Song, Zikai Yu, Junqing Chen, Yi-Ping Phoebe Yang, Wei Huazhong Univ Sci & Technol Wuhan Peoples R China La Trobe Univ Bundoora Vic Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks. Our project is available at https://***/SkyeSong38/CSWinTT.

关键词： Visualization Target tracking Image recognition Optimization methods computer architecture Benchmark testing Transformers

来源：评论

学校读者我要写书评

暂无评论

Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks

Exploring Patch-wise Semantic Relation for Contrastive Learn...

引用

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

作者： Jung, Chanyong Kwon, Gihyun Ye, Jong Chul Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol Kim Jaechul Grad Sch AI Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the methods often ignore the diverse semantic relation within the images. To address this, here we propose a novel semantic relation consistency (SRC) regularization along with the decoupled contrastive learning, which utilize the diverse semantics by focusing on the heterogeneous semantics between the image patches of a single image. To further improve the performance, we present a hard negative mining by exploiting the semantic relation. We verified our method for three tasks: single-modal and multi-modal image translations, and GAN compression task for image translation. Experimental results confirmed the state-of-art performance of our method in all the three tasks.

关键词： computer vision Image coding Computational modeling Semantics Focusing pattern recognition Task analysis

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 << < 84 85 86 87 88 89 90 91 92 93 > >>

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：