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...
29 篇 swiss fed inst t...
29 篇 sensetime res pe...
27 篇 university of sc...
27 篇 zhejiang univ pe...
26 篇 univ chinese aca...
26 篇 swiss fed inst t...
25 篇 university of ch...
24 篇 tsinghua univ pe...
24 篇 univ chinese aca...
24 篇 nanyang technol ...
24 篇 sun yat sen univ...
22 篇 peng cheng labor...
20 篇 shanghai ai lab ...
20 篇 korea adv inst s...
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
18 篇 radu timofte
16 篇 zha zheng-jun
14 篇 sun jian
12 篇 fan haoqiang
12 篇 chen wei-ting
12 篇 lei lei
12 篇 qiao yu
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 marcos v. conde
11 篇 liu shuai
11 篇 van de weijer jo...
10 篇 danelljan martin
10 篇 kim seon joo

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401-410 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：

Doubling down: sparse grounding with an additional, almost-matching caption for detection-oriented multimodal pretraining

Doubling down: sparse grounding with an additional, almost-m...

引用

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

作者： Nebbia, Giacomo Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A common paradigm in deep learning applications for computer vision is self-supervised pretraining followed by supervised fine-tuning on a target task. In the self-supervision step, a model is trained in a supervised fashion, but the source of supervision needs to be implicitly defined by the data. Image-caption alignment is often used as such a source of implicit supervision in multimodal pretraining, and grounding (i.e., matching word tokens with visual tokens) is one way to exploit it. We introduce a strategy to take advantage of an underexplored structure in image-caption datasets: the relationship between captions matched with different images but mentioning the same objects. Given an image-caption pair, we find an additional caption that mentions one of the objects the first caption mentions, and we impose a sparse grounding between the image and the second caption so that only a few word tokens are grounded in the image. Our goal is to learn a better feature representation for the objects mentioned by both captions, encouraging grounding between the additional caption and the image to focus on the common objects only. We report superior grounding performance when comparing our approach with a previously-published pretraining strategy, and we show the benefit of our proposed double-caption grounding on two downstream detection tasks: supervised detection and open-vocabulary detection.

关键词： Deep learning computer vision Visualization Grounding conferences Computational modeling Data models

来源：评论

学校读者我要写书评

暂无评论

Pseudo-label Generation for Agricultural Robotics Applications

Pseudo-label Generation for Agricultural Robotics Applicatio...

引用

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

作者： Ciarfuglia, Thomas A. Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza Univ Rome Dept Comp Sci Management & Automat Engn DIAG Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the context of table grape cultivation there is rising interest in robotic solutions for harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at the core of these systems require large amounts of labelled data, which in this context is often not available. In this work, we propose a semi-supervised solution to reduce the data needed to get state-of-the-art detection and segmentation of fruits in orchards. We present the case of table grape vineyards in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the concrete scenario where the source labelled data is wine grape, while the target data is table grape, with considerable covariate shift. Starting from a simple video input, our method generates first bounding box labels, leveraging the structure from motion information, then segmentation masks, using the same weakly generated bounding box labels and a refining step based on Grabcut. This system is able to produce labels that considerably reduce the covariate shift from source to target data and that requires very limited data acquisition effort. Comparisons with State-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images, with very simple labelling.

关键词： Structure from motion Motion segmentation Pipelines Refining Spraying Lighting pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Can the Mathematical Correctness of Object Configurations Affect the Accuracy of Their Perception?

Can the Mathematical Correctness of Object Configurations Af...

引用

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

作者： Jiang, Han Li, Zeqian Whitehill, Jacob Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We investigate a new type of dataset bias based on the mathematical correctness of object configurations in visual scenes, and how this bias can affect the accuracy of computer vision models. Our experiments demonstrate how CNNs trained to detect and recognize individual objects are capable of implicitly learning simple mathematical relationships between them directly from pixel data;moreover, models that are trained with a dataset bias (e.g., all examples are mathematically correct) can suffer in performance when evaluated on test data without this bias. We found evidence for this effect in two settings: (1) object detection of math symbols in images of arithmetic expressions, and (2) object detection of moving particles from images produced by a physics simulator. Importantly, the semantic bias that we study is based not just on simple co-occurrence patterns in each image, but rather on higher-order semantic rules that generalize to unique combinations of objects not seen during training. While the magnitude of the effect was small, the accuracy difference was statistically reliable.

关键词： Training computer vision Computational modeling Semantics Symbols Object detection Mathematical models

来源：评论

学校读者我要写书评

暂无评论

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

Adversarial Machine Learning Attacks Against Video Anomaly D...

引用

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

作者： Mumcu, Furkan Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on adversarial machine learning targeting video understanding models and no previous work which focuses on video anomaly detection. To this end, we investigate an adversarial machine learning attack against video anomaly detection systems, that can be implemented via an easy-to-perform cyber-attack. Since surveillance cameras are usually connected to the server running the anomaly detection model through a wireless network, they are prone to cyber-attacks targeting the wireless connection. We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems. Specifically, we apply several effects caused by the Wi-Fi deauthentication attack on video quality (e.g., slow down, freeze, fast forward, low resolution) to the popular benchmark datasets for video anomaly detection. Our experiments with several state-of-the-art anomaly detection models show that the attackers can significantly undermine the reliability of video anomaly detection systems by causing frequent false alarms and hiding physical anomalies from the surveillance system.

关键词： computer vision Computational modeling Wireless networks Video surveillance Adversarial machine learning Synchronization Servers

来源：评论

学校读者我要写书评

暂无评论

Searching for Energy-Efficient Hybrid Adder-Convolution Neural Networks

Searching for Energy-Efficient Hybrid Adder-Convolution Neur...

引用

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

作者： Li, Wenshuo Chen, Xinghao Bai, Jinyu Ning, Xuefei Wang, Yunhe Huawei Noahs Ark Lab Hong Kong Peoples R China Huawei TCS Lab Hong Kong Peoples R China Beihang Univ Sch Integrated Circuit Sci & Engn Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As convolutional neural networks (CNNs) are more and more widely used in computer vision area, the energy consumption of CNNs has become the focus of researchers. For edge devices, both the battery life and the inference latency are critical and directly affect user experience. Recently, great progress has been made in the design of neural architectures and new operators. The emergence of neural architecture search technology has improved the performance of network step by step, and liberated the productivity of engineers to a certain extent. New operators, such as AdderNets, make it possible to further improve the energy efficiency of neural networks. In this paper, we explore the fusion of new adder operators and common convolution operators into state-of-the-art light-weight networks, GhostNet, to search for models with better energy efficiency and performance. Our proposed search equilibrium strategy ensures that the adder and convolution operators can be treated fairly in the search, and the resulting model achieves the same accuracy of 73.9% with GhostNet on the ImageNet dataset at an extremely low power consumption of 0.612 mJ. When keeping the same energy consumption, the accuracy reaches 74.3% which is 0.4% higher than original GhostNet.

关键词： Training Energy consumption computer vision Convolution Computational modeling Neural networks computer architecture

来源：评论

学校读者我要写书评

暂无评论

Adversarial Robustness through the Lens of Convolutional Filters

Adversarial Robustness through the Lens of Convolutional Fil...

引用

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

作者： Gavrikov, Paul Keuper, Janis Offenburg Univ IMLA Offenburg Germany Fraunhofer ITWM CC HPC Kaiserslautern Germany Fraunhofer Res Ctr ML Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common defense mechanism is regularization through adversarial training which injects worst-case perturbations back into training to strengthen the decision boundaries, and to reduce overfitting. In this context, we perform an investigation of 3 x 3 convolution filters that form in adversarially-trained models. Filters are extracted from 71 public models of the l(infinity)-RobustBench CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from models built on the same architectures but trained without robust regularization. We observe that adversarially-robust models appear to form more diverse, less sparse, and more orthogonal convolution filters than their normal counterparts. The largest differences between robust and normal models are found in the deepest layers, and the very first convolution layer, which consistently and predominantly forms filters that can partially eliminate perturbations, irrespective of the architecture.

关键词： Training Convolution Perturbation methods computer architecture Predictive models Robustness Data models

来源：评论

学校读者我要写书评

暂无评论

Transformaly - Two (Feature Spaces) Are Better Than One

Transformaly - Two (Feature Spaces) Are Better Than One

引用

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

作者： Cohen, Matan Jacob Avidan, Shai Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Anomaly detection is a well-established research area that seeks to identify samples outside of a predetermined distribution. An anomaly detection pipeline is comprised of two main stages: (1) feature extraction and (2) normality score assignment. Recent papers used pre-trained networks for feature extraction achieving state-of-the-art results. However, the use of pre-trained networks does not fully-utilize the normal samples that are available at train time. This paper suggests taking advantage of this information by using teacher-student training. In our setting, a pretrained teacher network is used to train a student network on the normal training samples. Since the student network is trained only on normal samples, it is expected to deviate from the teacher network in abnormal cases. This difference can serve as a complementary representation to the pre-trained feature vector. Our method - Transformaly - exploits a pre-trained vision Transformer (ViT) to extract both feature vectors: the pre-trained (agnostic) features and the teacher-student (fine-tuned) features. We report state-of-the-art AUROC results in both the common unimodal setting, where one class is considered normal and the rest are considered abnormal, and the multimodal setting, where all classes but one are considered normal, and just one class is considered abnormal(1).

关键词： Training Visualization computer vision conferences Pipelines computer architecture Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Doppelganger Saliency: Towards More Ethical Person Re-Identification

Doppelganger Saliency: Towards More Ethical Person Re-Identi...

引用

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

作者： RichardWebster, Brandon Hu, Brian Fieldhouse, Keith Hoogs, Anthony Kitware Inc 1712 Route 9Suite 300 Clifton Pk NY 12065 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Modern surveillance systems have become increasingly dependent on artificial intelligence to provide actionable information for real-time decision making. A critical question relates to how these systems handle difficult ethical dilemmas, such as the re-identification of similar looking individuals. Potential misidentification of individuals can have severe negative consequences, as evidenced by recent headlines of individuals who were wrongly targeted for crimes they did not commit based on false matches. A computer vision-based saliency algorithm is proposed to help identify pixel-level differences in pairs of images containing visually similar individuals, which we term "doppelgangers." The computed saliency maps can alert human users of the presence of doppelg angers and provide important visual evidence to reduce the potential of false matches in these high-stakes situations. We show both qualitative and quantitative saliency results on doppelg angers found in a video-based person re-identification dataset (MARS) using three different state-of-the-art models. Our results suggest that this novel use of visual saliency can improve overall outcomes by helping human users in the person re-identification setting, while assuring the ethical and trusted operation of surveillance systems.

关键词： Ethics Visualization computer vision Surveillance conferences Computational modeling Decision making

来源：评论

学校读者我要写书评

暂无评论

CDAD: A Common Daily Action Dataset with Collected Hard Negative Samples

CDAD: A Common Daily Action Dataset with Collected Hard Nega...

引用

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

作者： Xiang, Wangmeng Li, Chao Li, Ke Wang, Biao Hua, Xian-Sheng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The research on action understanding has achieved significant progress with the establishment of various benchmark datasets. However, the results of action understanding are far from satisfactory in practice. One reason is that the existing action datasets ignore the existence of many hard negative samples in real-world scenarios, which are usually undefined confusion actions, e.g., holding a pen near the mouth vs. smoking. In this work, we focus on the common actions in our daily life and present a novel Common Daily Action Dataset (CDAD), which consists of 57,824 video clips of 23 well-defined common daily actions with rich manual annotations. Particularly, for each daily action, we collect not only diverse positive samples but also various hard negative samples that have minor differences (share similarities) in action with the positive ones. The established CDAD dataset could not only serve as a benchmark for several important daily action understanding tasks, including multi-label action recognition, temporal action localization, and spatial-temporal action detection, but also provide a testbed for researchers to investigate the influence of highly similar negative samples in learning action understanding models.

关键词： Location awareness computer vision Codes conferences Computational modeling Mouth Manuals

来源：评论

学校读者我要写书评

暂无评论

Retrieval-Augmented Open-Vocabulary Object Detection

Retrieval-Augmented Open-Vocabulary Object Detection

引用

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

作者： Kim, Jooyeon Cho, Eulrang Kim, Sehyung Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea Samsung Res Mountain View CA USA

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OVD) has been studied with vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, iPod, and alligator. To extend the previous methods in two aspects, we propose Retrieval-Augmented Losses and visual Features ( RALF). Our method retrieves related 'negative' classes and augments loss functions. Also, visual features are augmented with 'verbalized concepts' of classes, e.g., worn on the feet, handheld music player, and sharp teeth. Specifically, RALF consists of two modules: Retrieval Aug-mented Losses ( RAL) and Retrieval-Augmented visual Features (RAF). RAL constitutes two losses reflecting the semantic similarity with negative vocabularies. In addition, RAF augments visual features with the verbalized concepts from a large language model (LLM). Our experiments demonstrate the effectiveness of RALF on COCO and LVIS benchmark datasets. We achieve improvement up to 3.4 box APN on novel categories of the COCO dataset and 3.6 mask APr gains on the LVIS dataset. Code is available at https://***/mlvlab/RALF.

关键词： computer vision Object Detection Open-Vocabulary Object Detection Retrieval-Augmentation

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 << < 37 38 39 40 41 42 43 44 45 46 > >>

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：