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

20,994 篇 会议
99 册 图书
86 篇 期刊文献
1 篇 学位论文

馆藏范围

21,179 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,604 篇 工学
- 11,180 篇 计算机科学与技术...
- 2,631 篇 机械工程
- 2,543 篇 软件工程
- 990 篇 光学工程
- 848 篇 电气工程
- 676 篇 控制科学与工程
- 487 篇 信息与通信工程
- 242 篇 仪器科学与技术
- 215 篇 测绘科学与技术
- 159 篇 生物医学工程（可授...
- 150 篇 生物工程
- 139 篇 电子科学与技术（可...
- 69 篇 安全科学与工程
- 67 篇 化学工程与技术
- 55 篇 建筑学
- 53 篇 土木工程
- 43 篇 力学（可授工学、理...
- 41 篇 航空宇航科学与技...
3,462 篇 医学
- 3,452 篇 临床医学
- 41 篇 基础医学(可授医学...
2,484 篇 理学
- 1,248 篇 数学
- 1,213 篇 物理学
- 446 篇 统计学（可授理学、...
- 418 篇 生物学
- 269 篇 系统科学
- 67 篇 化学
424 篇 管理学
- 218 篇 管理科学与工程(可...
- 217 篇 图书情报与档案管...
- 43 篇 工商管理
144 篇 艺术学
- 142 篇 设计学（可授艺术学...
41 篇 法学
31 篇 农学
12 篇 经济学
10 篇 教育学
6 篇 文学
3 篇 军事学

主题

8,072 篇 computer vision
2,880 篇 pattern recognit...
2,859 篇 training
1,808 篇 computational mo...
1,718 篇 visualization
1,477 篇 cameras
1,381 篇 shape
1,374 篇 face recognition
1,364 篇 three-dimensiona...
1,342 篇 feature extracti...
1,269 篇 image segmentati...
1,156 篇 robustness
1,109 篇 semantics
982 篇 layout
977 篇 object detection
953 篇 computer archite...
952 篇 benchmark testin...
931 篇 codes
918 篇 object recogniti...
898 篇 computer science

机构

174 篇 univ sci & techn...
154 篇 carnegie mellon ...
149 篇 univ chinese aca...
144 篇 chinese univ hon...
110 篇 microsoft resear...
104 篇 zhejiang univ pe...
98 篇 swiss fed inst t...
93 篇 tsinghua univ pe...
92 篇 tsinghua univers...
90 篇 microsoft res as...
88 篇 shanghai ai lab ...
83 篇 zhejiang univers...
76 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
68 篇 shanghai jiao to...
68 篇 university of ch...
66 篇 google res mount...
66 篇 univ oxford oxfo...

作者

83 篇 van gool luc
71 篇 zhang lei
60 篇 timofte radu
49 篇 yang yi
49 篇 luc van gool
48 篇 xiaoou tang
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
41 篇 qi tian
37 篇 vasconcelos nuno
37 篇 liu yang
37 篇 chen xilin
37 篇 li fei-fei
36 篇 liu xiaoming
36 篇 shan shiguang
36 篇 li stan z.
36 篇 torralba antonio
33 篇 zhou jie

语言

21,138 篇 英文
31 篇 中文
5 篇 土耳其文
4 篇 其他
2 篇 日文

检索条件"任意字段=2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011"

共 21180 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：

Teaching Matters: Investigating the Role of Supervision in vision Transformers

Teaching Matters: Investigating the Role of Supervision in V...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Walmer, Matthew Suri, Saksham Gupta, Kamal Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have gained significant popularity in recent years and have proliferated into many applications. However, their behavior under different learning paradigms is not well explored. We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance. We also discover ViT behaviors that are consistent across supervision, including the emergence of Offset Local Attention Heads. These are self-attention heads that attend to a token adjacent to the current token with a fixed directional offset, a phenomenon that to the best of our knowledge has not been highlighted in any prior work. Our analysis shows that ViTs are highly flexible and learn to process local and global information in different orders depending on their training method. We find that contrastive self-supervised methods learn features that are competitive with explicitly supervised features, and they can even be superior for part-level tasks. We also find that the representations of reconstruction-based models show non-trivial similarity to contrastive self-supervised models.

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

Practical Network Acceleration with Tiny Sets

Practical Network Acceleration with Tiny Sets

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Guo-Hua Wu, Jianxin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

Due to data privacy issues, accelerating networks with tiny training sets has become a critical need in practice. Previous methods mainly adopt filter-level pruning to accelerate networks with scarce training samples. In this paper, we reveal that dropping blocks is a fundamentally superior approach in this scenario. It enjoys a higher acceleration ratio and results in a better latency-accuracy performance under the few-shot setting. To choose which blocks to drop, we propose a new concept namely recoverability to measure the difficulty of recovering the compressed network. Our recoverability is efficient and effective for choosing which blocks to drop. Finally, we propose an algorithm named PRACTISE to accelerate networks using only tiny sets of training images. PRACTISE outperforms previous methods by a significant margin. For 22% latency reduction, PRACTISE surpasses previous methods by on average 7% on ImageNet-1k. It also enjoys high generalization ability, working well under data-free or out-of-domain data settings, too. Our code is at https://***/DoctorKey/Practise.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Julia Barnett, Alina Jade Donnelly, Jon Kishore, Satvik Fang, Jerry Schwartz, Fides Regina Chen, Chaofan Lo, Joseph Y. Rudin, Cynthia Duke Univ Durham NC 27708 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (纸本)9798350365474

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： breast cancer cancer computer vision deep learning interpretability interpretable machine learning machine learning mammography medical imaging neural networks

来源：评论

学校读者我要写书评

暂无评论

1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions

1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense P...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yin, Dongshuo Yang, Yiran Wang, Zhechao Yu, Hongfeng Wei, Kaiwen Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Towards Professional Level Crowd Annotation of Expert Domain Data

Towards Professional Level Crowd Annotation of Expert Domain...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Pei Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350301298

Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Confidence-Aware RGB-D Face recognition via Virtual Depth Synthesis

Confidence-Aware RGB-D Face Recognition via Virtual Depth Sy...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Zijian Wang, Mei Deng, Weihong Shi, Hongzhi Wen, Dongchao Zhang, Yingjie Cui, Xingchen Zhao, Jian Inspur Beijing Elect Informat Ind Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. However, collecting sufficient paired RGB-D training data is expensive and time-consuming, hindering wide deployment. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining. To seamlessly integrate the two distinct networks and harness the complementary benefits of RGB and depth information for improved accuracy, we propose an innovative Adaptive Confidence Weighting (ACW). This mechanism is designed to learn confidence estimates for each modality to achieve modality fusion at the score level. Our method is simple and lightweight, only requiring ACW training beyond the backbone models. Experiments on multiple public RGB-D face recognition benchmarks demonstrate state-of-the-art performance surpassing previous methods based on depth estimation and feature fusion, validating the efficacy of our approach.

关键词： domain-independent pre-training modality fusion RGB-D face recognition

来源：评论

学校读者我要写书评

暂无评论

LightPainter: Interactive Portrait Relighting with Freehand Scribble

LightPainter: Interactive Portrait Relighting with Freehand ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mei, Yiqun Zhang, He Zhang, Xuaner Zhang, Jianming Shu, Zhixin Wang, Yilin Wei, Zijun Yan, Shi Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.

关键词： vision + graphics

来源：评论

学校读者我要写书评

暂无评论

MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

MoFusion: A Framework for Denoising-Diffusion-based Motion S...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dabral, Rishabh Mughal, Muhammad Hamza Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350301298

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature. We urge the reader to watch our supplementary video at https://***/projects/MoFusion/.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Senadeera, Damith Chamalke Yang, Xiaoyun Kollias, Dimitrios Slabaugh, Gregory Queen Mary Univ London Sch Elect Engn & Comp Sci London England Queen Marys Digital Environm Res Inst DERI London England Remark AI UK Ltd London England

ISBN: (纸本)9798350365474

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatio-temporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at (1).

关键词： computer vision Cropping Deep Learning Efficient Additive Attention UniFormerV2 Video Analytics Violence Detection

来源：评论

学校读者我要写书评

暂无评论

Iterative vision-and-Language Navigation

Iterative Vision-and-Language Navigation

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Krantz, Jacob Banerjee, Shurjo Zhu, Wang Corso, Jason Anderson, Peter Lee, Stefan Thomason, Jesse Oregon State Univ Corvallis OR 97331 USA Univ Michigan Ann Arbor MI USA Univ Southern Calif Los Angeles CA USA Google Res Sunnyvale CA USA

ISBN: (纸本)9798350301298

We present Iterative vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time. Existing vision-and-Language Navigation (VLN) benchmarks erase the agent's memory at the beginning of every episode, testing the ability to perform cold-start navigation with no prior information. However, deployed robots occupy the same environment for long periods of time. The IVLN paradigm addresses this disparity by training and evaluating VLN agents that maintain memory across tours of scenes that consist of up to 100 ordered instruction-following Room-to-Room (R2R) episodes, each defined by an individual language instruction and a target path. We present discrete and continuous Iterative Room-to-Room (IR2R) benchmarks comprising about 400 tours each in 80 indoor scenes. We find that extending the implicit memory of high-performing transformer VLN agents is not sufficient for IVLN, but agents that build maps can benefit from environment persistence, motivating a renewed focus on map-building agents in VLN.

关键词： language reasoning vision

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：