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

20,798 篇 会议
88 篇 期刊文献
65 册 图书

馆藏范围

20,950 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,275 篇 工学
- 10,923 篇 计算机科学与技术...
- 2,484 篇 机械工程
- 2,307 篇 软件工程
- 913 篇 光学工程
- 771 篇 电气工程
- 556 篇 控制科学与工程
- 405 篇 信息与通信工程
- 210 篇 测绘科学与技术
- 131 篇 生物医学工程（可授...
- 104 篇 电子科学与技术（可...
- 100 篇 生物工程
- 92 篇 仪器科学与技术
- 56 篇 化学工程与技术
- 52 篇 建筑学
- 48 篇 土木工程
- 44 篇 安全科学与工程
- 38 篇 力学（可授工学、理...
- 38 篇 航空宇航科学与技...
- 35 篇 交通运输工程
3,457 篇 医学
- 3,449 篇 临床医学
- 34 篇 基础医学(可授医学...
2,315 篇 理学
- 1,154 篇 数学
- 1,132 篇 物理学
- 417 篇 统计学（可授理学、...
- 386 篇 生物学
- 252 篇 系统科学
- 57 篇 化学
353 篇 管理学
- 184 篇 图书情报与档案管...
- 176 篇 管理科学与工程(可...
- 32 篇 工商管理
28 篇 法学
20 篇 农学
15 篇 教育学
9 篇 经济学
8 篇 艺术学
5 篇 文学
5 篇 军事学

主题

8,203 篇 computer vision
3,010 篇 pattern recognit...
2,732 篇 training
1,769 篇 computational mo...
1,657 篇 visualization
1,483 篇 cameras
1,415 篇 shape
1,369 篇 three-dimensiona...
1,369 篇 face recognition
1,285 篇 image segmentati...
1,272 篇 feature extracti...
1,178 篇 robustness
1,090 篇 semantics
1,040 篇 layout
1,007 篇 object detection
975 篇 object recogniti...
969 篇 computer science
946 篇 computer archite...
946 篇 benchmark testin...
931 篇 codes

机构

174 篇 univ sci & techn...
154 篇 carnegie mellon ...
148 篇 univ chinese aca...
144 篇 chinese univ hon...
113 篇 microsoft resear...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
97 篇 tsinghua univ pe...
93 篇 tsinghua univers...
91 篇 microsoft res as...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
76 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
69 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 google res mount...
66 篇 univ oxford oxfo...

作者

80 篇 van gool luc
71 篇 zhang lei
59 篇 timofte radu
48 篇 yang yi
47 篇 xiaoou tang
44 篇 darrell trevor
43 篇 tian qi
43 篇 luc van gool
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 qi tian
39 篇 li stan z.
37 篇 liu yang
37 篇 chen xilin
36 篇 shan shiguang
35 篇 liu xiaoming
35 篇 vasconcelos nuno
35 篇 torralba antonio
32 篇 zhou jie

语言

20,928 篇 英文
14 篇 中文
6 篇 其他
2 篇 日文
2 篇 土耳其文

检索条件"任意字段=2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009"

共 20951 条记录，以下是4991-5000 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：

GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos

GenHowTo: Learning to Generate Actions and State Transformat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Tomáš Souček Dima Damen Michael Wray Ivan Laptev Josef Sivic CIIRC CTU Czech Institute of Informatics Robotics and Cybernetics at the Czech Technical University in Prague. University of Bristol MBZUAI Mohamed bin Zayed University of Artificial Intelligence.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We address the task of generating temporally consistent and physically plausible images of actions and object state transformations. Given an input image and a text prompt describing the targeted transformation, our generated images preserve the environment and transform objects in the initial image. Our contributions are threefold. First, we leverage a large body of instructional videos and automatically mine a dataset of triplets of consecutive frames corresponding to initial object states, actions, and resulting object transformations. Second, equipped with this data, we develop and train a conditioned diffusion model dubbed GenHowTo. Third, we evaluate GenHowTo on a variety of objects and actions and show superior performance compared to existing methods. In particular, we introduce a quantitative evaluation where GenHowTo achieves 88% and 74% on seen and unseen interaction categories, respectively, outperforming prior work by a large margin.

关键词： computer vision Computational modeling Transforms Diffusion models pattern recognition Videos

来源：评论

学校读者我要写书评

暂无评论

Brain Decodes Deep Nets

Brain Decodes Deep Nets

引用

conference on computer vision and pattern recognition (cvpr)

作者： Huzheng Yang James Gee Jianbo Shi University of Pennsylvania

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First, explicit mapping between the brain and deep-network features across dimensions of space, layers, scales, and channels is crucial. This mapping method, Fac-torTopy, is plug-and-play for any deep-network; with it, one can paint a picture of the network onto the brain (liter-ally!). Second, our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior, growing with more data or network capacity. It also provides in-sight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchi-cally organized network suffer less from catastrophic for-getting after fine-tuned.

关键词： Training Technological innovation computer vision Image coding Computational modeling Data visualization Organizations

来源：评论

学校读者我要写书评

暂无评论

Learned Scanpaths Aid Blind Panoramic Video Quality Assessment

Learned Scanpaths Aid Blind Panoramic Video Quality Assessme...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Kanglong Fan Wen Wen Mu Li Yifan Peng Kede Ma City University of Hong Kong Harbin Institute of Technology Shenzhen The University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets, encompassing both synthetic and authentic distortions, validate the superiority of our blind PVQA model over existing methods.

关键词： Training Visualization computer vision Distortion Generators Quality assessment pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Context-Based and Diversity-Driven Specificity in Compositional Zero-Shot Learning

Context-Based and Diversity-Driven Specificity in Compositio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yun Li Zhe Liu Hang Chen Lina Yao CSIRO‘s Data61 Bytedance Ltd. Snap Inc.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize ‘Sliced-Strawberry’ over a generic ‘Red-Strawberry’, despite the former being more informative. They also suffer from ballooning search space when shifting from Close-World (CW) to Open-World (OW) CZSL. To address the issues, we introduce the Context-based and Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context. This novel approach allows for more accurate predictions by emphasizing specific attribute-object pairs and improves composition filtering in OW-CZSL. We conduct experiments in both CW and OW scenarios, and our model achieves state-of-the-art results across three datasets.

关键词： computer vision Accuracy Zero-shot learning Filtering Computational modeling pattern recognition

来源：评论

学校读者我要写书评

暂无评论

CapsuleRRT: Relationships-aware Regression Tracking via Capsules

CapsuleRRT: Relationships-aware Regression Tracking via Caps...

引用

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

作者： Ma, Ding Wu, Xiangqian Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781665445092

Regression tracking has gained more and more attention thanks to its easy-to-implement characteristics, while existing regression trackers rarely consider the relationships between the object parts and the complete object. This would ultimately result in drift from the target object when missing some parts of the target object. Recently, Capsule Network (CapsNet) has shown promising results for image classification benefits from its part-object relationships mechanism, while CapsNet is known for its high computational demand even when carrying out simple tasks. Therefore, a primitive adaptation of CapsNet to regression tracking does not make sense, since this will seriously affect speed of a tracker. To solve these problems, we first explore the spatial-temporal relationships endowed by the CapsNet for regression tracking. The entire regression framework, dubbed CapsuleRRT, consists of three parts. One is S-Caps, which captures the spatial relationships between the parts and the object. Meanwhile, a T-Caps module is designed to exploit the temporal relationships within the target. The response of the target is obtained by STCaps Learning. Further, a priorguided capsule routing algorithm is proposed to generate more accurate capsule assignments for subsequent frames. Apart from this, the heavy computation burden in CapsNet is addressed with a knowledge distillation pose matrix compression strategy that exploits more tight and discriminative representation with few samples. Extensive experimental results show that CapsuleRRT performs favorably against state-of-the-art methods in terms of accuracy and speed.

关键词： computer vision Target tracking Meetings Routing pattern recognition Task analysis Image classification

来源：评论

学校读者我要写书评

暂无评论

vision Transformers are Good Mask Auto-Labelers

Vision Transformers are Good Mask Auto-Labelers

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shiyi Lan Xitong Yang Zhiding Yu Zuxuan Wu Jose M. Alvarez Anima Anandkumar NVIDIA Meta AI FAIR Fudan University Caltech

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. We show that vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2022 Burst Super-Resolution Challenge

NTIRE 2022 Burst Super-Resolution Challenge

引用

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

作者： Bhat, Goutam Danelljan, Martin Timofte, Radu Cao, Yizhen Cao, Yuntian Chen, Meiya Chen, Xihao Cheng, Shen Dudhane, Akshay Fan, Haoqiang Gang, Ruipeng Gao, Jian Gu, Yan Huang, Jie Huang, Liufeng Jo, Youngsu Kang, Sukju Khan, Salman Khan, Fahad Shahbaz Kondo, Yuki Li, Chenghua Li, Fangya Li, Jinjing Li, Youwei Li, Zechao Liu, Chenming Liu, Shuaicheng Liu, Zikun Liu, Zhuoming Luo, Ziwei Luo, Zhengxiong Mehta, Nancy Murala, Subrahmanyam Nam, Yoonchan Nakatani, Chihiro Ostyakov, Pavel Pan, Jinshan Song, Ge Sun, Jian Sun, Long Tang, Jinhui Ukita, Norimichi Wen, Zhihong Wu, Qi Wu, Xiaohe Xiao, Zeyu Xiong, Zhiwei Xu, Rongjian Xu, Ruikang Yan, Youliang Yang, Jialin Yang, Wentao Yang, Zhongbao Yasue, Fuma Yao, Mingde Yu, Lei Zhang, Cong Zamir, Syed Waqas Zhang, Jianxing Zhang, Shuohao Zhang, Zhilu Zheng, Qian Zhou, Gaofeng Zhussip, Magauiya Zou, Xueyi Zuo, Wangmeng Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Commun Univ China Beijing Peoples R China UHDTV Res & Applicat Lab Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China South China Univ Technol Guangzhou Peoples R China Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China UESTC Chengdu Peoples R China Xiaomi Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China SRC B Beijing Peoples R China CASIA Beijing Peoples R China Indian Inst Technol Ropar IIT Ropar Rupnagar Punjab India Mohamed Bin Zayed Univ AI MBZUAI Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates MBZUAI Abu Dhabi U Arab Emirates Australian Natl Univ ANU Canberra ACT Australia Linkoping Univ Linkoping Sweden Toyota Technol Inst TTI Nagoya Aichi Japan Sogang Univ Seoul South Korea UESTC Chengdu Peoples R China USTC Hefei Peoples R China WHU Wuhan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.

关键词： Training Photography Superresolution Noise reduction Network architecture Transformers pattern recognition

来源：评论

学校读者我要写书评

暂无评论

SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection

SRDAN: Scale-aware and Range-aware Domain Adaptation Network...

引用

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

作者： Zhang, Weichen Li, Wen Xu, Dong Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Univ Elect Sci & Technol China Sch Comp Sci & Engn Langfang Peoples R China

ISBN: (纸本)9781665445092

Geometric characteristic plays an important role in the representation of an object in 3D point clouds. For example, large objects often contain more points, while small ones contain fewer points. The points from objects near the capture device are denser, while those from far-range objects are sparser. These issues bring new challenges to 3D object detection, especially under the domain adaptation scenarios. In this work, we propose a new cross-dataset 3D object detection method named Scale-aware and Range-aware Domain Adaptation Network (SRDAN). We take advantage of the geometric characteristics of 3D data (i.e., size and distance), and propose the scale-aware domain alignment and the range-aware domain alignment strategies to guide the distribution alignment between two domains. For scale-aware domain alignment, we design a 3D voxel-based feature pyramid network to extract multi-scale semantic voxel features, and align the features and instances with similar scales between two domains. For range-aware domain alignment, we introduce a range-guided domain alignment module to align the features of objects according to their distance to the capture device. Extensive experiments under three different scenarios demonstrate the effectiveness of our SRDAN approach, and comprehensive ablation study also validates the importance of geometric characteristics for cross-dataset 3D object detection.

关键词： computer vision Three-dimensional displays Semantics Object detection Feature extraction pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

InstanceDiffusion: Instance-Level Control for Image Generation

InstanceDiffusion: Instance-Level Control for Image Generati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Xudong Wang Trevor Darrell Sai Saketh Rambhatla Rohit Girdhar Ishan Misra GenAI Meta UC Berkeley

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instance-level control to text-to-image diffusion models. InstanceDiffusion supports free-form language conditions per instance and allows flexible ways to specify instance locations such as simple single points, scribbles, bounding boxes or intricate instance segmentation masks, and combinations thereof We propose three major changes to text-to-image models that enable precise instance-level control. Our UniFusion block enables instance-level conditions for text-to-image models, the ScaleU block improves image fidelity, and our Multi-instance Sampler improves generations for multiple instances. InstanceDiffusion significantly surpasses specialized state-of-the-art models for each location condition. Notably, on the COCO dataset, we out-perform previous state-of-the-art by 20.4% $AP_{50}^{box}$ for box inputs, and 25.4% IoU for mask inputs.

关键词： Instance segmentation computer vision Image synthesis Computational modeling Text to image Diffusion models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding

Deep Learning based Spatial-Temporal In-loop filtering for V...

引用

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

作者： Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665448994

The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.

关键词： Video coding Optical filters Filtering Delays Decoding pattern recognition Convolutional neural networks

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：