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

23,008 篇 会议
126 册 图书
94 篇 期刊文献

馆藏范围

23,227 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,631 篇 工学
- 11,116 篇 计算机科学与技术...
- 3,481 篇 软件工程
- 2,445 篇 机械工程
- 1,716 篇 光学工程
- 1,080 篇 电气工程
- 1,014 篇 控制科学与工程
- 788 篇 信息与通信工程
- 411 篇 仪器科学与技术
- 352 篇 生物工程
- 251 篇 生物医学工程（可授...
- 196 篇 电子科学与技术（可...
- 114 篇 化学工程与技术
- 109 篇 安全科学与工程
- 100 篇 测绘科学与技术
- 88 篇 建筑学
- 88 篇 交通运输工程
- 84 篇 土木工程
3,495 篇 医学
- 3,482 篇 临床医学
- 82 篇 基础医学(可授医学...
3,246 篇 理学
- 1,941 篇 物理学
- 1,643 篇 数学
- 563 篇 统计学（可授理学、...
- 500 篇 生物学
- 249 篇 系统科学
- 106 篇 化学
521 篇 管理学
- 311 篇 图书情报与档案管...
- 223 篇 管理科学与工程(可...
- 76 篇 工商管理
276 篇 艺术学
- 276 篇 设计学（可授艺术学...
66 篇 法学
- 63 篇 社会学
38 篇 农学
28 篇 教育学
22 篇 经济学
10 篇 军事学
3 篇 文学

主题

10,186 篇 computer vision
3,967 篇 pattern recognit...
3,005 篇 training
2,007 篇 computational mo...
1,818 篇 visualization
1,815 篇 cameras
1,515 篇 feature extracti...
1,481 篇 shape
1,455 篇 three-dimensiona...
1,438 篇 image segmentati...
1,287 篇 robustness
1,206 篇 computer archite...
1,155 篇 semantics
1,147 篇 conferences
1,107 篇 layout
1,092 篇 computer science
1,088 篇 object detection
1,025 篇 benchmark testin...
970 篇 codes
922 篇 face recognition

机构

136 篇 univ sci & techn...
121 篇 univ chinese aca...
118 篇 chinese univ hon...
105 篇 carnegie mellon ...
101 篇 tsinghua univers...
101 篇 microsoft resear...
95 篇 swiss fed inst t...
93 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
79 篇 university of ch...
77 篇 shanghai ai lab ...
72 篇 shanghai jiao to...
69 篇 national laborat...
67 篇 microsoft res as...
67 篇 alibaba grp peop...
64 篇 adobe research
60 篇 peking univ peop...
60 篇 tsinghua univ pe...
59 篇 univ oxford oxfo...

作者

81 篇 van gool luc
72 篇 timofte radu
65 篇 zhang lei
47 篇 luc van gool
40 篇 yang yi
40 篇 li stan z.
37 篇 loy chen change
35 篇 chen chen
33 篇 xiaoou tang
32 篇 liu yang
32 篇 qi tian
31 篇 tian qi
31 篇 sun jian
30 篇 murino vittorio
29 篇 ling haibin
29 篇 darrell trevor
29 篇 pascal fua
29 篇 li fei-fei
28 篇 li xin
28 篇 ying shan

语言

22,989 篇 英文
210 篇 其他
22 篇 中文
5 篇 土耳其文
2 篇 日文

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

共 23228 条记录，以下是4651-4660 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：

Neural Architecture Search with Random Labels

Neural Architecture Search with Random Labels

引用

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

作者： Zhang, Xuanyang Hou, Pengfei Zhang, Xiangyu Sun, Jian MEGVII Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we investigate a new variant of neural architecture search (NAS) paradigm - searching with random labels (RLNAS). The task sounds counter-intuitive for most existing NAS algorithms since random label provides few information on the performance of each candidate architecture. Instead, we propose a novel NAS framework based on ease-of-convergence hypothesis, which requires only random labels during searching. The algorithm involves two steps: first, we train a SuperNet using random labels;second, from the SuperNet we extract the sub-network whose weights change most significantly during the training. Extensive experiments are evaluated on multiple datasets (e.g. NAS-Bench-201 and ImageNet) and multiple search spaces (e.g. DARTS-like and MobileNet-like). Very surprisingly, RLNAS achieves comparable or even better results compared with state-of-the-art NAS methods such as PC-DARTS, Single Path One-Shot, even though the counterparts utilize full ground truth labels for searching. We hope our finding could inspire new understandings on the essential of NAS.

关键词： Training computer vision computer architecture pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Playable Video Generation

Playable Video Generation

引用

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

作者： Menapace, Willi Lathuiliere, Stephane Tulyakov, Sergey Siarohin, Aliaksandr Ricci, Elisa Univ Trento Trento Italy Telecom Paris LTC Paris France Inst Polytech Paris Paris France Snap Inc Santa Monica CA USA Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety.

关键词： computer vision Codes computer architecture Games pattern recognition Task analysis Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Controlling the Rain: from Removal to Rendering

Controlling the Rain: from Removal to Rendering

引用

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

作者： Ni, Siqi Cao, Xueyun Yue, Tao Hu, Xuemei Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665445092

Existing rain image editing methods focus on either removing rain from rain images or rendering rain on rain-free images. This paper proposes to realize continuous control of rain intensity bidirectionally, from clear rain-free to downpour image with a single rain image as input, without changing the scene-specific characteristics, e.g. the direction, appearance and distribution of rain. Specifically, we introduce a Rain Intensity Controlling Network (RICNet) that contains three sub-networks of background extraction network, high-frequency rain-streak elimination network and main controlling network, which allows to control rain image of different intensities continuously by interpolation in the deep feature space. The HOG loss and autocorrelation loss are proposed to enhance consistency in orientation and suppress repetitive rain streaks. Furthermore, a decremental learning strategy that trains the network from downpour to drizzle images sequentially is proposed to further improve the performance and speedup the convergence. Extensive experiments on both rain dataset and real rain images demonstrate the effectiveness of the proposed method.

关键词： Interpolation computer vision Rain Aerospace electronics Rendering (computer graphics) Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Learning by Planning: Language-Guided Global Image Editing

Learning by Planning: Language-Guided Global Image Editing

引用

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

作者： Shi, Jing Xu, Ning Xu, Yihang Bui, Trung Dernoncourt, Franck Xu, Chenliang Univ Rochester Rochester NY 14627 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Recently, language-guided global image editing draws increasing attention with growing application potentials. However, previous GAN-based methods are not only confined to domain-specific, low-resolution data but also lacking in interpretability. To overcome the collective difficulties, we develop a text-to-operation model to map the vague editing language request into a series of editing operations, e.g., change contrast, brightness, and saturation. Each operation is interpretable and differentiable. Furthermore, the only supervision in the task is the target image, which is insufficient for a stable training of sequential decisions. Hence, we propose a novel operation planning algorithm to generate possible editing sequences from the target image as pseudo ground truth. Comparison experiments on the newly collected MA5k-Req dataset and GIER dataset show the advantages of our methods. Code is available at https://***/jshi31/T2ONet.

关键词： Training computer vision Codes Brightness Planning pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Robust Reflection Removal with Reflection-free Flash-only Cues

Robust Reflection Removal with Reflection-free Flash-only Cu...

引用

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

作者： Lei, Chenyang Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. We observe that this flash-only image is visually reflection-free, and thus it can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a dedicated model that not only utilizes the reflection-free cue but also avoids introducing artifacts, which helps accurately estimate reflection and transmission. Our experiments on real-world images with various types of reflection demonstrate the effectiveness of our model with reflection-free flash-only cues: our model outperforms state-of-the-art reflection removal approaches by more than 5.23dB in PSNR, 0.04 in SSIM, and 0.068 in LPIPS. Our source code and dataset are publicly available at ***/ChenyangLEI/flash-reflection-removal.

关键词： computer vision Codes Computational modeling computer architecture Reflection pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations

Continual Semantic Segmentation via Repulsion-Attraction of ...

引用

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

作者： Michieli, Umberto Zanuttigh, Pietro Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781665445092

Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new categories are made available over time while previous training data is not retained. The proposed continual learning scheme shapes the latent space to reduce forgetting whilst improving the recognition of novel classes. Our framework is driven by three novel components which we also combine on top of existing techniques effortlessly. First, prototypes matching enforces latent space consistency on old classes, constraining the encoder to produce similar latent representation for previously seen classes in the subsequent steps. Second, features sparsification allows to make room in the latent space to accommodate novel classes. Finally, contrastive learning is employed to cluster features according to their semantics while tearing apart those of different classes. Extensive evaluation on the Pascal VOC2012 and ADE20K datasets demonstrates the effectiveness of our approach, significantly outperforming state-of-the-art methods.

关键词： Deep learning computer vision Limiting Shape Semantics Prototypes Training data

来源：评论

学校读者我要写书评

暂无评论

MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection

MeGA-CDA: Memory Guided Attention for Category-Aware Unsuper...

引用

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

作者： Vibashan, V. S. Gupta, Vikram Oza, Poojan Sindagi, Vishwanath A. Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Mercedes Benz Res & Dev India Bengaluru India

ISBN: (纸本)9781665445092

Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA). The proposed method consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features. However, since the category information is not available for the target samples, we propose to generate memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator. The proposed method is evaluated on several benchmark datasets and is shown to outperform existing approaches.

关键词： Training computer vision Object detection Benchmark testing Feature extraction Routing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Kaleido-BERT: vision-Language Pre-training on Fashion Domain

Kaleido-BERT: Vision-Language Pre-training on Fashion Domain

引用

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

作者： Zhuge, Mingchen Gao, Dehong Fan, Deng-Ping Jin, Linbo Chen, Ben Zhou, Haoming Qiu, Minghui Shao, Ling Alibaba Grp Hangzhou Peoples R China Incept Inst AI IIAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers. In contrast to random masking strategy of recent VL models, we design alignment guided masking to jointly focus more on image-text semantic relations. To this end, we carry out five novel tasks, i.e., rotation, jigsaw, camouflage, grey-to-color, and blank-to-color for self-supervised VL pre-training at patches of different scale. Kaleido-BERT is conceptually simple and easy to extend to the existing BERT framework, it attains state-of-the-art results by large margins on four downstream tasks, including text retrieval (R@1: 4.03% absolute improvement), image retrieval (R@1: 7.13% abs imv.), category recognition (ACC: 3.28% abs imv.), and fashion captioning (Bleu4: 1.2 abs imv.). We validate the efficiency of Kaleido-BERT on a wide range of e-commerical websites, demonstrating its broader potential in real-world applications.

关键词： computer vision Image recognition Text recognition Semantics Image retrieval Bit error rate Transformers

来源：评论

学校读者我要写书评

暂无评论

Home Action Genome: Cooperative Compositional Action Understanding

Home Action Genome: Cooperative Compositional Action Underst...

引用

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

作者： Rai, Nishant Chen, Haofeng Ji, Jingwei Desai, Rishi Kozuka, Kazuki Ishizaka, Shun Adeli, Ehsan Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA Panasonic Corp Kadoma Osaka Japan

ISBN: (纸本)9781665445092

Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomicactions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn representations capturing this information. However, there remains a lack of studies that extend action composition and leverage multiple viewpoints and multiple modalities of data for representation learning. To promote research in this direction, we introduce Home Action Genome (HOMAGE): a multi-view action dataset with multiple modalities and view-points supplemented with hierarchical activity and atomic action labels together with dense scene composition labels. Leveraging rich multi-modal and multi-view settings, we propose Cooperative Compositional Action Understanding (CCAU), a cooperative learning framework for hierarchical action recognition that is aware of compositional action elements. CCAU shows consistent performance improvements across all modalities. Furthermore, we demonstrate the utility of co-learning compositions in few-shot action recognition by achieving 28.6% mAP with just a single sample.

关键词： Location awareness Learning systems computer vision Annotations Image color analysis Genomics Data visualization

来源：评论

学校读者我要写书评

暂无评论

Towards Real-World Blind Face Restoration with Generative Facial Prior

Towards Real-World Blind Face Restoration with Generative Fa...

引用

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

作者： Wang, Xintao Li, Yu Zhang, Honglun Shan, Ying Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.

关键词： Geometry computer vision Art Limiting Image color analysis Face recognition Transforms

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 << < 462 463 464 465 466 467 468 469 470 471 > >>

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：