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

5,349 篇 会议
128 册 图书
29 篇 期刊文献

馆藏范围

5,506 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,747 篇 工学
- 2,253 篇 计算机科学与技术...
- 926 篇 软件工程
- 478 篇 光学工程
- 399 篇 控制科学与工程
- 332 篇 机械工程
- 261 篇 仪器科学与技术
- 250 篇 信息与通信工程
- 128 篇 电气工程
- 122 篇 生物工程
- 90 篇 生物医学工程（可授...
- 70 篇 电子科学与技术（可...
- 39 篇 安全科学与工程
- 33 篇 化学工程与技术
- 31 篇 建筑学
- 27 篇 土木工程
- 25 篇 交通运输工程
- 24 篇 航空宇航科学与技...
858 篇 理学
- 526 篇 物理学
- 439 篇 数学
- 155 篇 统计学（可授理学、...
- 133 篇 生物学
- 28 篇 化学
- 21 篇 系统科学
242 篇 艺术学
- 242 篇 设计学（可授艺术学...
196 篇 管理学
- 101 篇 图书情报与档案管...
- 97 篇 管理科学与工程(可...
- 27 篇 工商管理
96 篇 医学
- 96 篇 临床医学
- 21 篇 基础医学(可授医学...
28 篇 法学
- 26 篇 社会学
8 篇 教育学
6 篇 经济学
5 篇 农学
2 篇 军事学

主题

2,165 篇 computer vision
915 篇 pattern recognit...
890 篇 conferences
640 篇 training
565 篇 cameras
410 篇 feature extracti...
373 篇 computational mo...
364 篇 image segmentati...
350 篇 visualization
338 篇 face recognition
321 篇 computer archite...
309 篇 robustness
290 篇 computer science
287 篇 shape
280 篇 humans
266 篇 object detection
234 篇 layout
196 篇 neural networks
191 篇 application soft...
191 篇 lighting

机构

20 篇 swiss fed inst t...
19 篇 institute for co...
18 篇 university of sc...
18 篇 microsoft resear...
18 篇 swiss fed inst t...
16 篇 carnegie mellon ...
16 篇 chinese acad sci...
14 篇 tsinghua univers...
14 篇 univ sci & techn...
14 篇 carnegie mellon ...
12 篇 harbin inst tech...
12 篇 tsinghua univ pe...
12 篇 department of co...
11 篇 megvii technol p...
11 篇 computer vision ...
11 篇 computer vision ...
10 篇 indian statistic...
10 篇 comp vis ctr bar...
10 篇 school of comput...
10 篇 univ modena & re...

作者

58 篇 timofte radu
17 篇 van gool luc
17 篇 luc van gool
17 篇 horst bischof
16 篇 rita cucchiara
16 篇 radu timofte
14 篇 escalera sergio
14 篇 bischof horst
14 篇 sergio escalera
13 篇 zhigang zhu
13 篇 hugo jair escala...
12 篇 murino vittorio
12 篇 li stan z.
12 篇 chen wei-ting
12 篇 qiang ji
11 篇 cucchiara rita
11 篇 fan haoqiang
11 篇 umapada pal
11 篇 marcos v. conde
11 篇 bowyer kevin w.

语言

5,492 篇 英文
10 篇 中文
5 篇 其他

检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"

共 5506 条记录，以下是601-610 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：

Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Modi, Rajat Rana, Aayush Jung Kumar, Akash Tirupattur, Praveen Vyas, Shruti Rawat, Yogesh Singh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665487399

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

来源：评论

学校读者我要写书评

暂无评论

Learning Generalized Feature for Temporal Action Detection: Application for Natural Driving Action recognition Challenge

Learning Generalized Feature for Temporal Action Detection: ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chuong Nguyen Ngoc Nguyen Su Huynh Vinh Nguyen Son Nguyen CyberCore AI Morioka Iwate Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports our approach for the 2022 AI City Challenge - Naturalistic Driving Action recognition (Track 3), where the objective is to detect when and what kinds of actions that a driver performs in a long, untrimmed video. Our solution is built upon the single stage ActionFormer detector, in which temporal location and classification are predicted simultaneously for efficiency. The input feature for the detector is extracted offline using our proposed backbone, which we named "ConvNext-Video". However, due to the small size of the dataset, training the model to avoid over-fitting becomes challenging. To address this problem, we focus on training techniques that can improve the generalization of underlying features. Specifically, we utilize two methods: "learning without forgetting" and semi-weak supervised learning on the unlabeled data A2. Finally, we also add a second-stage classifier (SSC) using our ConvNeXt-Video backbone. The SSC Classifer is designed to combine information from multi-clips and multi-view cameras to improve the prediction precision. Our best result achieves 29.1 F1 score on the public test set. Our source code is released at link.

关键词： Training conferences Urban areas Supervised learning Detectors Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Z-Domain Entropy Adaptable Flex for Semi-supervised Action recognition in the Dark

Z-Domain Entropy Adaptable Flex for Semi-supervised Action R...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Zhi Fan, Zijun Li, Yongjie Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The subtask of Human Action recognition (AR) in the dark is gaining a lot of traction nowadays, which takes a significant place in the field of computer vision. The implementation of its application includes self-driving at night, human-pose estimation, night surveillance, etc. Currently, solutions such as DLN for AR have emerged. However, due to the poor accuracy even when leveraging on large amounts of datasets and complex architectures, the development of AR in the dark has been slow to progress. In this paper, we propose a novel and straightforward method: Z-Domain Entropy Adaptable Flex. This constructs a neural network architecture R(2+1)D, including (i) a self-attention mechanism, which combines and extracts corresponding and complementary features from the dual pathways;(ii) Zero-DCE low light image enhancement, which improves enhanced quality;and (iii) FlexMatch method, which can generates the pseudo-labels flexibly. With the help of pseudo-labels from FlexMatch, our proposed Z-DEAF method facilitates the process of gaining desired classification boundaries. This works by repeating Expanding Entropy and Shrinking Entropy. It aims to solve the problem of unclear classification boundaries between the categories. Our model obtains superior performance in experiments, and achieves state-of-the-art results on ARID.

关键词： computer vision Flexible printed circuits Surveillance Neural networks Estimation computer architecture Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cieslak, Mikolaj Govindarajan, Umabharathi Garcia, Alejandro Chandrashekar, Anuradha Haedrich, Torsten Mendoza-Drosik, Aleksander Michels, Dominik L. Pirk, Soeren Fu, Chia-Chun Palubicki, Wojciech GreenMatterAI Berlin Germany Blue River Technol Santa Clara CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tech Univ Darmstadt Darmstadt Germany Christian Albrecht Univ Kiel Kiel Germany Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350365474

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

OmniFlow: Human Omnidirectional Optical Flow

OmniFlow: Human Omnidirectional Optical Flow

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Seidel, Roman Apitzsch, Andre Hirtz, Gangolf Tech Univ Chemnitz Fac Elect Engn & Informat Technol D-09126 Chemnitz Germany

ISBN: (纸本)9781665448994

Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: https://***/byfs

关键词： Training Three-dimensional displays Lighting Estimation Network architecture Rendering (computer graphics) Indoor environment

来源：评论

学校读者我要写书评

暂无评论

An Implicit Spatiotemporal Shape Model for Human Activity Localization and recognition

An Implicit Spatiotemporal Shape Model for Human Activity Lo...

引用

IEEE-computer-Society conference on computer vision and pattern recognition workshops

作者： Oikonomopoulos, A. Patras, I. Pantic, M. Univ London Imperial Coll Sci Technol & Med Dept Comp London England Queen Mary Univ London Dept Elect Engn London England

ISBN: (纸本)9781424439942

In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic, sparse, 'visual words' and 'visual verbs'. Evidence for the spatiotemporal localization of the activity are accumulated in a probabilistic spatiotemporal voting scheme. The local nature of our voting framework allows us to recover multiple activities that take place in the same scene, as well as activities in the presence of clutter and occlusions. We construct class-specific codebooks using the descriptors in the training set, where we take the spatial co-occurrences of pairs of codewords into account. The positions of the codeword pairs with respect to the object centre, as well as the frame in the training set in which they occur are subsequently stored in order to create a spatiotemporal model of codeword co-occurrences. During the testing phase, we use Mean Shift Mode estimation in order to spatially segment the subject that performs the activities in every frame, and the Radon transform in order to extract the most probable hypotheses concerning the temporal segmentation of the activities within the continuous stream.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Training to Improve Player and Ball Detection in Soccer

Semi-Supervised Training to Improve Player and Ball Detectio...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vandeghen, Renaud Cioppa, Anthony Van Droogenbroeck, Marc Univ Liege Liege Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%.

关键词： Training Adaptation models Annotations Benchmark testing Data models pattern recognition Proposals

来源：评论

学校读者我要写书评

暂无评论

Group Leakage Overestimates Performance: A Case Study in Keystroke Dynamics

Group Leakage Overestimates Performance: A Case Study in Key...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ayotte, Blaine Banavar, Mahesh K. Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn 8 Clarkson Ave Potsdam NY 13699 USA

ISBN: (纸本)9781665448994

Keystroke dynamics is a powerful behavioral biometric capable of user authentication based on typing patterns. As larger keystroke datasets become available, machine learning and deep learning algorithms are becoming popular. Knowledge of every possible impostor is not known during training which means that keystroke dynamics is an open set recognition problem. Treating open set recognition problems as closed set (assuming samples from all impostors are present) can cause models to incur data leakage, which can provide unrealistic overestimates of performance. It is a common problem in machine learning and can cause models to report higher accuracies than would be expected in the real world. In this paper, we outline open set recognition and discuss how, if not handled properly, it can lead to data leakage. The performance of common machine learning methods, such as SVM and MLP are investigated with and without leakage to clearly demonstrate the differences in performance. A synthetic dataset and a publicly available keystroke dynamics fixed-text dataset are used for research transparency and reproducibility.

关键词： Training Support vector machines Deep learning computer vision Machine learning algorithms Heuristic algorithms conferences

来源：评论

学校读者我要写书评

暂无评论

SOFEA: A Non-iterative and Robust Optical Flow Estimation Algorithm for Dynamic vision Sensors

SOFEA: A Non-iterative and Robust Optical Flow Estimation Al...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Low, Weng Fei Gao, Zhi Xiang, Cheng Ramesh, Bharath Natl Univ Singapore 1 Inst Hlth Singapore Singapore

ISBN: (纸本)9781728193601

We introduce the single-shot optical flow estimation algorithm (SOFEA) to non-iteratively compute the continuous-time flow information of events produced from bio-inspired cameras such as the dynamic vision sensor (DVS). The output of a DVS is a stream of asynchronous spikes ("events"), transmitted at very minimal latency (110 mu s), caused by local brightness changes. Due to this unconventional output, a continuous representation of events over time is invaluable to most applications using the DVS. To this end, SOFEA consolidates the spatio-temporal information on the surface of active events for flow estimation in a single-shot manner, as opposed to iterative methods in the literature. In contrast to previous works, this is also the first principled method towards finding locally optimal set of neighboring events for plane fitting using an adaptation of Prim's algorithm. Consequently, SOFEA produces flow estimates that are more accurate across a wide variety of scenes compared to state-of-the-art methods. A direct application of such flow estimation is rendering sharp event images using the set of active events at a given time, which is further demonstrated and compared to existing works (source code will be made available at our homepage after the review process).

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Network Amplification with Efficient MACs Allocation

Network Amplification with Efficient MACs Allocation

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Chuanjian Han, Kai Xiao, An Nie, Ying Zhang, Wei Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracies, such as EfficientNet and RegNet. These works try to enlarge the network architecture with one unified rule by sampling and statistical methods. However, the rule is not prospective to the design of large networks because it is obtained from the experience of researchers on small network architectures. In this paper, we propose to enlarge the capacity of CNN models by fine-grained MACs allocation for the width, depth and resolution on the stage level. In particular, starting from a base small model, we gradually add extra channels, layers or resolution by using a dynamic programming manner. With step-by-step modifying the computations on different stages, the enlarged network will be equipped with optimal allocation and utilization of MACs. On EfficientNet, our method consistently outperforms the performance of the original scaling method. In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80.9% and 84.3% ImageNet top-1 accuracies under the setting of 600M and 4.4B MACs, respectively.

关键词： Statistical analysis Computational modeling Heuristic algorithms Neural networks Network architecture pattern recognition Dynamic programming

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 << < 57 58 59 60 61 62 63 64 65 66 > >>

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：