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

6,421 篇 会议
25 篇 期刊文献
3 册 图书

馆藏范围

6,448 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,849 篇 工学
- 3,647 篇 计算机科学与技术...
- 1,431 篇 软件工程
- 790 篇 光学工程
- 302 篇 信息与通信工程
- 242 篇 控制科学与工程
- 219 篇 电气工程
- 201 篇 机械工程
- 80 篇 生物医学工程（可授...
- 68 篇 生物工程
- 67 篇 电子科学与技术（可...
- 64 篇 仪器科学与技术
- 36 篇 建筑学
- 33 篇 力学（可授工学、理...
- 33 篇 土木工程
- 33 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 22 篇 交通运输工程
- 20 篇 材料科学与工程（可...
- 18 篇 化学工程与技术
1,453 篇 理学
- 945 篇 物理学
- 890 篇 数学
- 352 篇 统计学（可授理学、...
- 134 篇 生物学
- 38 篇 系统科学
- 23 篇 化学
160 篇 管理学
- 110 篇 图书情报与档案管...
- 52 篇 管理科学与工程(可...
- 25 篇 工商管理
112 篇 医学
- 112 篇 临床医学
17 篇 法学
- 17 篇 社会学
12 篇 农学
8 篇 教育学
7 篇 艺术学
6 篇 经济学
2 篇 军事学

主题

2,288 篇 computer vision
789 篇 pattern recognit...
637 篇 cameras
629 篇 computer science
568 篇 face recognition
555 篇 layout
510 篇 image segmentati...
509 篇 conferences
498 篇 shape
445 篇 robustness
439 篇 object recogniti...
388 篇 humans
332 篇 feature extracti...
321 篇 training
303 篇 object detection
262 篇 image recognitio...
257 篇 application soft...
246 篇 lighting
238 篇 image reconstruc...
237 篇 computational mo...

机构

41 篇 microsoft resear...
26 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
20 篇 department of co...
19 篇 swiss fed inst t...
19 篇 carnegie mellon ...
18 篇 department of co...
17 篇 department of in...
17 篇 the robotics ins...
17 篇 institute of com...
16 篇 univ sci & techn...
16 篇 robotics institu...
15 篇 tsinghua univ pe...
14 篇 department of el...
14 篇 school of comput...
14 篇 school of comput...
13 篇 univ maryland co...
13 篇 microsoft resear...
13 篇 microsoft resear...

作者

39 篇 timofte radu
28 篇 s.k. nayar
24 篇 huang thomas s.
23 篇 xiaoou tang
22 篇 t. kanade
20 篇 t.s. huang
19 篇 van gool luc
19 篇 t. darrell
19 篇 chellappa rama
18 篇 nayar shree k.
17 篇 a.k. jain
17 篇 a. zisserman
17 篇 jain anil k.
16 篇 g. healey
16 篇 torralba antonio
16 篇 heung-yeung shum
16 篇 zisserman andrew
16 篇 l. van gool
15 篇 m. shah
15 篇 ji qiang

语言

6,447 篇 英文
2 篇 其他

检索条件"任意字段=1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992"

共 6449 条记录，以下是511-520 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：

Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation

Multiple Degradation and Reconstruction Network for Single I...

引用

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

作者： Li, Juncheng Yang, Hanhui Yi, Qiaosi Fang, Faming Gao, Guangwei Zeng, Tieyong Zhang, Guixu Chinese Univ Hong Kong Hong Kong Peoples R China East China Normal Univ Shanghai Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of parameters, which greatly limits their application scenarios. Different from previous works that blindly increase the depth of the network, we explore the degradation mechanism of the noisy image and propose a lightweight Multiple Degradation and Reconstruction Network (MDRN) to progressively remove noise. Meanwhile, we propose two novel Heterogeneous Knowledge Distillation Strategies (HMDS) to enable MDRN to learn richer and more accurate features from heterogeneous models, which make it possible to reconstruct higher-quality denoised images under extreme conditions. Extensive experiments show that our MDRN achieves favorable performance against other SID models with fewer parameters. Meanwhile, plenty of ablation studies demonstrate that the introduced HMDS can improve the performance of tiny models or the model under high noise levels, which is extremely useful for related applications.

关键词： Degradation Knowledge engineering Computational modeling Resists pattern recognition Image restoration Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Focused Feature Differentiation Network for Image Quality Assessment

Focused Feature Differentiation Network for Image Quality As...

引用

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

作者： He, Gang Wang, Yong Xu, Li Zhang, Wenli Sun, Ming Wen, Xing Xidian Univ Xian Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image quality assessment (IQA) intended to assess the perceptual quality of images has been an essential problem in both human and machine vision. Recently, with the help of deep neural network (DNN), IQA algorithms can extract more valuable differences between the distorted and reference images than the traditional algorithms, and thus the performance of DNN-based algorithms is more satisfactory than that of previous algorithms. However, the accuracy for different distorted images preference rating of the existing DNN-based quality assessment methods will be decreased when multiple distorted images are quite similar to each other or to the reference image. To tackle this problem, we propose a focused feature differentiation network (FFDN) to highlight the feature maps with greater distorted and reference differentiation. Furthermore, we use the multi-scale feature fusion module to fuse the focused differentiation features at different scale receptive fields. To further improve the accuracy of our method, we predict the mean opinion score and differentiation score by stages and combine them with different self-learning weights. Finally, we convert the weighted score into different image preference degrees. Experimental results on the validation dataset of CLIC2022 and test dataset of CLIC2021 show that the accuracy of our model FFDN is higher than other excellent quality assessment methods.

关键词： Image quality Deep learning Fuses Convolution Machine vision conferences Neural networks

来源：评论

学校读者我要写书评

暂无评论

M2FNet: Multi-modal Fusion Network for Emotion recognition in Conversation

M2FNet: Multi-modal Fusion Network for Emotion Recognition i...

引用

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

作者： Chudasama, Vishal Kar, Purbayan Gudmalwar, Ashish Shah, Nirmesh Wasnik, Pankaj Onoe, Naoyuki Sony Res India Media Anal Grp Bangalore Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Emotion recognition in Conversations (ERC) is crucial in developing sympathetic human-machine interaction. In conversational videos, emotion can be present in multiple modalities, i.e., audio, video, and transcript. However, due to the inherent characteristics of these modalities, multi-modal ERC has always been considered a challenging undertaking. Existing ERC research focuses mainly on using text information in a discussion, ignoring the other two modalities. We anticipate that emotion recognition accuracy can be improved by employing a multi-modal approach. Thus, in this study, we propose a Multi-modal Fusion Network (M2FNet) that extracts emotion-relevant features from visual, audio, and text modality. It employs a multi-head attention-based fusion mechanism to combine emotion-rich latent representations of the input data. We introduce a new feature extractor to extract latent features from the audio and visual modality. The proposed feature extractor is trained with a novel adaptive margin-based triplet loss function to learn emotion-relevant features from the audio and visual data. In the domain of ERC, the existing methods perform well on one benchmark dataset but not on others. Our results show that the proposed M2FNet architecture outperforms all other methods in terms of weighted average F1 score on well-known MELD and IEMOCAP datasets and sets a new state-of-the-art performance in ERC.

关键词： Human computer interaction Emotion recognition Visualization Adaptation models Benchmark testing Feature extraction Robustness

来源：评论

学校读者我要写书评

暂无评论

Towards Exemplar-Free Continual Learning in vision Transformers: an Account of Attention, Functional and Weight Regularization

Towards Exemplar-Free Continual Learning in Vision Transform...

引用

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

作者： Pelosin, Francesco Jha, Saurav Torsello, Andrea Raducanu, Bogdan van de Weijer, Joost Ca Foscari Univ Venice Italy Univ New South Wales Sydney NSW Australia Comp Vis Ctr Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we investigate the continual learning of vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) - while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners.(1)

关键词： Learning systems Weight measurement Image recognition Surgery Benchmark testing Transformers Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Transformer for Single Image Super-Resolution

Transformer for Single Image Super-Resolution

引用

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

作者： Lu, Zhisheng Li, Juncheng Liu, Hong Huang, Chaoyan Zhang, Linlin Zeng, Tieyong Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. However, most existing studies focus on building more complex networks with a massive number of layers. Recently, more and more researchers start to explore the application of Transformer in computer vision tasks. However, the heavy computational cost and high GPU memory occupation of the vision Transformer cannot be ignored. In this paper, we propose a novel Efficient Super-Resolution Transformer (ESRT) for SISR. ESRT is a hybrid model, which consists of a Lightweight CNN Backbone (LCB) and a Lightweight Transformer Backbone (LTB). Among them, LCB can dynamically adjust the size of the feature map to extract deep features with a low computational costs. LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA). Extensive experiments show that ESRT achieves competitive results with low computational cost. Compared with the original Transformer which occupies 16,057M GPU memory, ESRT only occupies 4,191M GPU memory. All codes are available at https://***/luissen/ESRT.

关键词： computer vision Adaptation models Computational modeling Superresolution Memory management Graphics processing units Transformers

来源：评论

学校读者我要写书评

暂无评论

HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes

HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes

引用

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

作者： Zhang, Fan You, Shaodi Li, Yu Fu, Ying Beijing Inst Technol Beijing Peoples R China Univ Amsterdam Amsterdam Netherlands Int Digital Econ Acad Shenzhen Peoples R China Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Intrinisic image decomposition (IID) aims to recover the reflectance and shading components from images and is the prerequisite to many downstream computer vision applications, such as image editing and image relighting. Due to the inherent difficulty in acquiring ground truth reflectance and shading, existing datasets are either synthetic indoor scenes or objects using graphics rendering (e.g., CGIntrinsics and ShapeNet etc.) or real photos with very sparse manual annotation (e.g., IIW and SAW etc.). Accompanied with the complex nature of outdoor scenes, most IID methods focus on the decomposition of indoor environment. There is still a long way to go before we can handle IID of outdoor scenes. In this paper, we take the attempt to perform intrinsic image decomposition for outdoor scenes when RGB image is not the only thing we can get from the enviroment. With the observation of prior work where nir-infrared (NIR) images are transparent to a range of colourants/dyes, we propose to extend it to more spectra by collecting hyperspectral imaging (HSI) data which are well aligned with RGB images and to perform IID with both of them. We also apply existing mainstream IID methods for comparison to examine current progress and challenges at the road towards IID outdoors. We still make some improvements and find problems when performing IID for outdoor scenes, even though we do not handle it perfectly. The data we collect will be made publicly available for further potential investigation.

关键词： Reflectivity computer vision Roads Surface acoustic waves Manuals Rendering (computer graphics) Image decomposition

来源：评论

学校读者我要写书评

暂无评论

A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds

A Closer Look at Blind Super-Resolution: Degradation Models,...

引用

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

作者： Zhang, Wenlong Shi, Guangyuan Liu, Yihao Dong, Chao Wu, Xiao-Ming HongKong Polytech Univ Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Degradation models play an important role in Blind super-resolution (SR). The classical degradation model, which mainly involves blur degradation, is too simple to simulate real-world scenarios. The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world. To address this problem, we propose a unified gated degradation model to generate a broad set of degradation cases using a random gate controller. Based on the gated degradation model, we propose simple baseline networks that can effectively handle non-blind, classical, practical degradation cases as well as many other corner cases. To fairly evaluate the performance of our baseline networks against state-of-the-art methods and understand their limits, we introduce the performance upper bound of an SR network for every degradation type. Our empirical analysis shows that with the unified gated degradation model, the proposed baselines can achieve much better performance than existing methods in quantitative and qualitative results, which are close to the performance upper bounds.

关键词： Degradation Analytical models computer vision Upper bound conferences Superresolution Logic gates

来源：评论

学校读者我要写书评

暂无评论

A New Dataset and Transformer for Stereoscopic Video Super-Resolution

A New Dataset and Transformer for Stereoscopic Video Super-R...

引用

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

作者： Imani, Hassan Islam, Md Baharul Wong, Lai-Kuan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-resolution video by reconstructing the high-resolution video. The key challenges in SVSR are preserving the stereo-consistency and temporal-consistency, without which viewers may experience 3D fatigue. There are several notable works on stereoscopic image super-resolution, but there is little research on stereo video super-resolution. In this paper, we propose a novel Transformer-based model for SVSR, namely Trans-SVSR. Trans-SVSR comprises two key novel components: a spatio-temporal convolutional self-attention layer and an optical flow-based feed-forward layer that discovers the correlation across different video frames and aligns the features. The parallax attention mechanism (PAM) that uses the cross-view information to consider the significant disparities is used to fuse the stereo views. Due to the lack of a benchmark dataset suitable for the SVSR task, we collected a new stereoscopic video dataset, SVSR-Set, containing 71 full high-definition (HD) stereo videos captured using a professional stereo camera. Extensive experiments on the collected dataset, along with two other datasets, demonstrate that the Trans-SVSR can achieve competitive performance compared to the state-of-the-art methods. Project code and additional results are available at https://***/H-deep/Trans-SVSR/.

关键词： Three-dimensional displays Stereo image processing Superresolution Transformers Optical imaging pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

引用

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

作者： Ornhag, Marcus Valtonen Persson, Patrik Wadenback, Marten Astrom, Kalle Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Linkoping Univ Dept Elect Engn Linkoping Sweden

ISBN: (纸本)9781665487399

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups. The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods.

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

来源：评论

学校读者我要写书评

暂无评论

CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

CFA: Constraint-based Finetuning Approach for Generalized Fe...

引用

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

作者： Guirguis, Karim Hendawy, Ahmed Eskandar, George Abdelsamad, Mohamed Kayser, Matthias Beyerer, Juergen Robert Bosch GmbH Stuttgart Germany Univ Stuttgart Stuttgart Germany Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (纸本)9781665487399

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

关键词： Learning systems Adaptation models computer vision conferences Object detection Search problems pattern recognition

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 << < 48 49 50 51 52 53 54 55 56 57 > >>

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：