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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 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
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 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 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,319 篇 英文
1 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2671-2680 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：

HOICLIP: Efficient Knowledge Transfer for HOI Detection with vision-Language Models

HOICLIP: Efficient Knowledge Transfer for HOI Detection with...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shan Ning Longtian Qiu Yongfei Liu Xuming He ShanghaiTech University Shanghai China ByteDance Inc. Shanghai Engineering Research Center of Intelligent Vision and Imaging

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human- object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://***/Artanic30/HOICLIP.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Densely Self-guided Wavelet Network for Image Denoising

Densely Self-guided Wavelet Network for Image Denoising

引用

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

作者： Liu, Wei Yan, Qiong Zhao, Yuzhi SenseTime Res Hong Kong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

During the past years, deep convolutional neural networks have achieved impressive success in image denoising. In this paper, we propose a densely self-guided wavelet network (DSWN) for real-world image denoising. The basic structure of DSWN is a top-down self-guidance architecture which is able to efficiently incorporate multi- scale information and extract good local features to recover clean images. Moreover, such a structure requires a smaller number of parameters and enables us to achieve better effectiveness than Unet structure. To avoid information loss and achieve a better receptive field size, we embed wavelet transform into DSWN. In addition, we apply densely residual learning to convolution blocks to enhance the feature extraction capability of the proposed network. At the full resolution level of DSWN, we adopt a double branch structure to generate the final output. One branch of them tends to pay attention to dark areas and the other performs better on bright areas. Such a double branch strategy is able to handle the noise at different exposures. The proposed network is validated by BSD68, Kodak24 and SIDD+ benchmark. Additional experimental results show that the proposed network outperforms most state-of-the-art image denoising solutions.

关键词： computer vision pattern recognition conferences

来源：评论

学校读者我要写书评

暂无评论

Open-Set Fine-Grained Retrieval via Prompting vision-Language Evaluator

Open-Set Fine-Grained Retrieval via Prompting Vision-Languag...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shijie Wang Jianlong Chang Haojie Li Zhihui Wang Wanli Ouyang Qi Tian International School of Information Science & Engineering Dalian University of Technology China Huawei Cloud & AI China College of Computer and Engineering Shandong University of Science and Technology China Sense Time Computer Vision Research Group The University of Sydney Australia

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works focus on close-set visual concepts, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the categoryspecific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. Quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches

Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches

引用

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

作者： Agarwal, Shruti Farid, Hany Fried, Ohad Agrawala, Maneesh Univ Calif Berkeley Berkeley CA 94720 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781728193601

Recent advances in machine learning and computer graphics have made it easier to convincingly manipulate video and audio. These so-called deep-fake videos range from complete full-face synthesis and replacement (face-swap), to complete mouth and audio synthesis and replacement (lip-sync), and partial word-based audio and mouth synthesis and replacement. Detection of deep fakes with only a small spatial and temporal manipulation is particularly challenging. We describe a technique to detect such manipulated videos by exploiting the fact that the dynamics of the mouth shape - visemes - are occasionally inconsistent with a spoken phoneme. We focus on the visemes associated with words having the sound M (mama), B (baba), or P (papa) in which the mouth must completely close in order to pronounce these phonemes. We observe that this is not the case in many deep-fake videos. Such phonemeviseme mismatches can, therefore, be used to detect even spatially small and temporally localized manipulations. We demonstrate the efficacy and robustness of this approach to detect different types of deep-fake videos, including in-the-wild deep fakes.

关键词： computer graphics

来源：评论

学校读者我要写书评

暂无评论

Improving Commonsense in vision-Language Models via Knowledge Graph Riddles

Improving Commonsense in Vision-Language Models via Knowledg...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shuquan Ye Yujia Xie Dongdong Chen Yichong Xu Lu Yuan Chenguang Zhu Jing Liao City University of Hong Kong Microsoft

This paper focuses on analyzing and improving the commonsense ability of recent popular vision-language (VL) models. Despite the great success, we observe that existing VL-models still lack commonsense knowledge/reasoning ability (e.g., “Lemons are sour”), which is a vital component towards artificial general intelligence. Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective. Rather than collecting a new VL training dataset, we propose a more scalable strategy, i.e., “Data Augmentation with kNowledge graph linearization for CommonsensE capability” (DANCE). It can be viewed as one type of data augmentation technique, which can inject commonsense knowledge into existing VL datasets on the fly during training. More specifically, we leverage the commonsense knowledge graph (e.g., ConceptNet) and create variants of text description in VL datasets via bidirectional sub-graph sequentialization. For better commonsense evaluation, we further propose the first retrieval-based commonsense diagnostic benchmark. By conducting extensive experiments on some representative VL-models, we demonstrate that our DANCE technique is able to significantly improve the commonsense ability while maintaining the performance on vanilla retrieval tasks. The code and data are available at https://***/pleaseconnectwifi/DANCE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DNDNet: Reconfiguring CNN for Adversarial Robustness

DNDNet: Reconfiguring CNN for Adversarial Robustness

引用

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

作者： Goel, Akhil Agarwal, Akshay Vatsa, Mayank Singh, Richa Ratha, Nalini K. IIIT Delhi Delhi India IIT Jodhpur Jodhpur Rajasthan India IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Several successful adversarial attacks have demonstrated the vulnerabilities of deep learning algorithms. These attacks are detrimental in building deep learning based dependable AI applications. Therefore, it is imperative to build a defense mechanism to protect the integrity of deep learning models. In this paper, we present a novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings. The parameter-free defense layer, when applied to any convolutional network, helps in achieving protection against attacks such as FGSM, L2, Elastic-Net, and DeepFool. Experiments are performed with different CNN architectures, including VGG, ResNet, and DenseNet, on three databases, namely, MNIST, CIFAR-10, and PaSC. The results showcase the efficacy of the proposed defense layer without adding any computational overhead. For example, on the CIFAR-10 database, while the attack can reduce the accuracy of the ResNet-50 model to as low as 6.3%, the proposed "defense layer" retains the original accuracy of 81.32%.

关键词： Mathematical model Perturbation methods Machine learning computer architecture Robustness Computational modeling Databases

来源：评论

学校读者我要写书评

暂无评论

Bilinear Parameterization For Differentiable Rank-Regularization

Bilinear Parameterization For Differentiable Rank-Regulariza...

引用

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

作者： Ornhag, Marcus Valtonen Olsson, Carl Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781728193601

Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former approach results in differentiable problems that can be efficiently optimized using local quadratic approximation, the latter is typically not differentiable (sometimes even discontinuous) and requires first order subgradient or splitting methods. It is well known that gradient based methods exhibit slow convergence for ill-conditioned problems. In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This allows us to use standard second order methods, such as Levenberg-Marquardt (LM) and Variable Projection (VarPro), to achieve accurate solutions for ill-conditioned cases. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than competing state-of-the-art methods.(1)

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： André s Romero Luc Van Gool Radu Timofte Computer Vision Lab ETH Z&#x00FC rich KU Leuven University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the downsampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Turning Strengths into Weaknesses: A Certified Robustness Inspired Attack Framework against Graph Neural Networks

Turning Strengths into Weaknesses: A Certified Robustness In...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Binghui Wang Meng Pang Yun Dong Department of Computer Science Illinois Institute of Technology School of Mathematics and Computer Sciences Nanchang University Department of Visual and Performing Arts Education and Sciences Waubonsee Community College

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph learning tasks. However, recent studies show that GNNs are vulnerable to both test-time evasion and training-time poisoning attacks that perturb the graph structure. While existing attack methods have shown promising attack performance, we would like to design an attack framework to further enhance the performance. In particular, our attack framework is inspired by certified robustness, which was originally used by defenders to defend against adversarial attacks. We are the first, from the attacker perspective, to leverage its properties to better attack GNNs. Specifically, we first derive nodes' certified perturbation sizes against graph evasion and poisoning attacks based on randomized smoothing, respectively. A larger certified perturbation size of a node indicates this node is theoretically more robust to graph perturbations. Such a property motivates us to focus more on nodes with smaller certified perturbation sizes, as they are easier to be attacked after graph perturbations. Accordingly, we design a certified robustness inspired attack loss, when incorporated into (any) existing attacks, produces our certified robustness inspired attack counterpart. We apply our frame-work to the existing attacks and results show it can significantly enhance the existing base attacks' performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo

Adaptive Patch Deformation for Textureless-Resilient Multi-V...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yuesong Wang Zhaojie Zeng Tao Guan Wei Yang Zhuo Chen Wenkai Liu Luoyuan Xu Yawei Luo School of Computer Science & Technology Huazhong University of Science & Technology School of Computer Science & Technology Zhejiang University

In recent years, deep learning-based approaches have shown great strength in multi-view stereo because of their outstanding ability to extract robust visual features. However, most learning-based methods need to build the cost volume and increase the receptive field enormously to get a satisfactory result when dealing with large-scale textureless regions, consequently leading to prohibitive memory consumption. To ensure both memory-friendly and textureless-resilient, we innovatively transplant the spirit of deformable convolution from deep learning into the traditional PatchMatch-based method. Specifically, for each pixel with matching ambiguity (termed unreliable pixel), we adaptively deform the patch centered on it to extend the receptive field until covering enough correlative reliable pixels (without matching ambiguity) that serve as anchors. When performing PatchMatch, constrained by the anchor pixels, the matching cost of an unreliable pixel is guaranteed to reach the global minimum at the correct depth and therefore increases the robustness of multi-view stereo significantly. To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds. As a result, our method achieves state-of-the-art performance on ETH3D and Tanks and Temples while preserving low memory consumption.

关键词：

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 << < 264 265 266 267 268 269 270 271 272 273 > >>

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：