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

22,774 篇 会议
111 篇 期刊文献
23 册 图书

馆藏范围

22,907 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,400 篇 工学
- 10,881 篇 计算机科学与技术...
- 3,451 篇 软件工程
- 2,430 篇 机械工程
- 1,722 篇 光学工程
- 1,011 篇 控制科学与工程
- 997 篇 电气工程
- 762 篇 信息与通信工程
- 394 篇 仪器科学与技术
- 337 篇 生物工程
- 257 篇 生物医学工程（可授...
- 215 篇 电子科学与技术（可...
- 113 篇 化学工程与技术
- 113 篇 安全科学与工程
- 98 篇 测绘科学与技术
- 94 篇 交通运输工程
- 86 篇 建筑学
- 82 篇 土木工程
3,362 篇 医学
- 3,348 篇 临床医学
- 79 篇 基础医学(可授医学...
3,252 篇 理学
- 1,953 篇 物理学
- 1,665 篇 数学
- 567 篇 统计学（可授理学、...
- 484 篇 生物学
- 245 篇 系统科学
- 110 篇 化学
507 篇 管理学
- 299 篇 图书情报与档案管...
- 220 篇 管理科学与工程(可...
- 75 篇 工商管理
252 篇 艺术学
- 252 篇 设计学（可授艺术学...
62 篇 法学
- 59 篇 社会学
40 篇 农学
25 篇 教育学
19 篇 经济学
11 篇 军事学
3 篇 文学

主题

10,127 篇 computer vision
4,025 篇 pattern recognit...
2,900 篇 training
1,958 篇 computational mo...
1,792 篇 cameras
1,759 篇 visualization
1,485 篇 shape
1,466 篇 image segmentati...
1,448 篇 feature extracti...
1,412 篇 three-dimensiona...
1,288 篇 robustness
1,169 篇 computer archite...
1,144 篇 layout
1,141 篇 computer science
1,134 篇 semantics
1,070 篇 object detection
1,043 篇 conferences
1,009 篇 benchmark testin...
967 篇 codes
810 篇 face recognition

机构

135 篇 univ sci & techn...
118 篇 univ chinese aca...
118 篇 chinese univ hon...
110 篇 carnegie mellon ...
99 篇 tsinghua univers...
99 篇 microsoft resear...
94 篇 swiss fed inst t...
92 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 university of ch...
72 篇 shanghai jiao to...
68 篇 microsoft res as...
65 篇 national laborat...
65 篇 alibaba grp peop...
64 篇 tsinghua univ pe...
63 篇 adobe research
60 篇 peking univ peop...
59 篇 peng cheng labor...

作者

78 篇 van gool luc
72 篇 timofte radu
63 篇 zhang lei
45 篇 luc van gool
40 篇 yang yi
37 篇 loy chen change
33 篇 xiaoou tang
33 篇 li stan z.
33 篇 qi tian
32 篇 sun jian
31 篇 liu yang
31 篇 li fei-fei
30 篇 chen chen
30 篇 tian qi
30 篇 pascal fua
29 篇 darrell trevor
28 篇 ying shan
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 hanqing lu

语言

22,831 篇 英文
50 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22908 条记录，以下是4441-4450 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：

Adaptive Consistency Prior based Deep Network for Image Denoising

Adaptive Consistency Prior based Deep Network for Image Deno...

引用

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

作者： Ren, Chao He, Xiaohai Wang, Chuncheng Zhao, Zhibo Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China

ISBN: (纸本)9781665445092

Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network. First, by introducing a non-linear filtering operator, a reliability matrix, and a high-dimensional feature transformation function into the traditional consistency prior, we propose a novel adaptive consistency prior (ACP). Second, by incorporating the ACP term into the maximum a posteriori framework, a model-based denoising method is proposed. This method is further used to inform the network design, leading to a novel end-to-end trainable and interpretable deep denoising network, called DeamNet. Note that the unfolding process leads to a promising module called dual element-wise attention mechanism (DEAM) module. To the best of our knowledge, both our ACP constraint and DEAM module have not been reported in the previous literature. Extensive experiments verify the superiority of DeamNet on both synthetic and real noisy image datasets.

关键词： Maximum a posteriori estimation Adaptation models computer vision Filtering Noise reduction pattern recognition Reliability

来源：评论

学校读者我要写书评

暂无评论

Gated Spatio-Temporal Attention-Guided Video Deblurring

Gated Spatio-Temporal Attention-Guided Video Deblurring

引用

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

作者： Suin, Maitreya Rajagopalan, A. N. Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781665445092

Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur. Most of the existing works depend on implicit or explicit alignment for temporal information fusion, which either increases the computational cost or results in suboptimal performance due to misalignment. In this work, we investigate two key factors responsible for deblurring quality: how to fuse spatio-temporal information and from where to collect it. We propose a factorized gated spatio-temporal attention module to perform non-local operations across space and time to fully utilize the available information without depending on alignment. First, we perform spatial aggregation followed by a temporal aggregation step. Next, we adaptively distribute the global spatio-temporal information to each pixel. It shows superior performance compared to existing non-local fusion techniques while being considerably more efficient. To complement the attention module, we propose a reinforcement learning-based framework for selecting keyframes from the neighborhood with the most complementary and useful information. Moreover, our adaptive approach can increase or decrease the frame usage at inference time, depending on the user's need. Extensive experiments on multiple datasets demonstrate the superiority of our method.

关键词： computer vision Adaptation models Fuses Computational modeling Logic gates pattern recognition Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising

Recorrupted-to-Recorrupted: Unsupervised Deep Learning for I...

引用

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

作者： Pang, Tongyao Zheng, Huan Quan, Yuhui Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665445092

Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising. In the last few years, there is an increasing interest in developing unsupervised deep denoisers which only call unorganized noisy images without ground truth for training. Nevertheless, the performance of these unsupervised deep denoisers is not competitive to their supervised counterparts. Aiming at developing a more powerful unsupervised deep denoiser, this paper proposed a data augmentation technique, called recorrupted-to-recorrupted (R2R), to address the overfitting caused by the absence of truth images. For each noisy image, we showed that the cost function defined on the noisy/noisy image pairs constructed by the R2R method is statistically equivalent to its supervised counterpart defined on the noisy/truth image pairs. Extensive experiments showed that the proposed R2R method noticeably outperformed existing unsupervised deep denoisers, and is competitive to representative supervised deep denoisers.

关键词： Training Deep learning computer vision AWGN Noise reduction Cost function pattern recognition

来源：评论

学校读者我要写书评

暂无评论

PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

PANDA: Adapting Pretrained Features for Anomaly Detection an...

引用

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

作者： Reiss, Tal Cohen, Niv Bergman, Liron Hoshen, Yedid Hebrew Univ Jerusalem Sch Comp Sci & Engn Jerusalem Israel

ISBN: (纸本)9781665445092

Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pre-trained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pre-trained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods. In order to obtain further performance gains in anomaly detection, we adapt pre-trained features to the target distribution. Although transfer learning methods are well established in multi-class classification problems, the one-class classification (OCC) setting is not as well explored. It turns out that naive adaptation methods, which typically work well in supervised learning, often result in catastrophic collapse (feature deterioration) and reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates using specialized architectures, but this limits the adaptation performance gain. We propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. Our method, PANDA, outperforms the state-of-the-art in the OCC, outlier exposure and anomaly segmentation settings by large margins(1).

关键词： computer vision Transfer learning Supervised learning computer architecture Performance gain Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Semantic Line Combination Detector

Semantic Line Combination Detector

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jinwon Ko Dongkwon Jin Chang-Su Kim Korea University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First, we generate various line combinations from reliable lines. Second, we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. More-over, it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection, symmetry axis detection, and composition-based image retrieval. Our codes are available at https://***/Jinwon-Ko/SLCD.

关键词： computer vision Codes Semantics Image retrieval Detectors pattern recognition Reliability

来源：评论

学校读者我要写书评

暂无评论

General Instance Distillation for Object Detection

General Instance Distillation for Object Detection

引用

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

作者： Dai, Xing Jiang, Zeren Wu, Zhao Bao, Yiping Wang, Zhicheng Liu, Si Zhou, Erjin MEGVII Technol Beijing Peoples R China BeiHang Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

In recent years, knowledge distillation has been proved to be an effective solution for model compression. This approach can make lightweight student models acquire the knowledge extracted from cumbersome teacher models. However, previous distillation methods of detection have weak generalization for different detection frameworks and rely heavily on ground truth (GT), ignoring the valuable relation information between instances. Thus, we propose a novel distillation method for detection tasks based on discriminative instances without considering the positive or negative distinguished by GT, which is called general instance distillation (GID). Our approach contains a general instance selection module (GISM) to make full use of feature-based, relation-based and response-based knowledge for distillation. Extensive results demonstrate that the student model achieves significant AP improvement and even outperforms the teacher in various detection frameworks. Specifically, RetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which surpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based teacher model with 38.1% AP.

关键词： computer vision Adaptation models 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

Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning

Test-Time Fast Adaptation for Dynamic Scene Deblurring via M...

引用

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

作者： Chi, Zhixiang Wang, Yang Yu, Yuanhao Tang, Jin Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9781665445092

In this paper, we tackle the problem of dynamic scene deblurring. Most existing deep end-to-end learning approaches adopt the same generic model for all unseen test images. These solutions are sub-optimal, as they fail to utilize the internal information within a specific image. On the other hand, a self-supervised approach, SelfDeblur, enables internal training within a test image from scratch, but it does not fully take advantage of large external datasets. In this work, we propose a novel selfsupervised meta-auxiliary learning to improve the performance of deblurring by integrating both external and internal learning. Concretely, we build a self-supervised auxiliary reconstruction task that shares a portion of the network with the primary deblurring task. The two tasks are jointly trained on an external dataset. Furthermore, we propose a meta-auxiliary training scheme to further optimize the pretrained model as a base learner, which is applicable for fast adaptation at test time. During training, the performance of both tasks is coupled. Therefore, we are able to exploit the internal information at test time via the auxiliary task to enhance the performance of deblurring. Extensive experimental results across evaluation datasets demonstrate the effectiveness of test-time adaptation of the proposed method.

关键词： Training Adaptation models computer vision Computational modeling Performance gain pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Global2Local: Efficient Structure Search for Video Action Segmentation

Global2Local: Efficient Structure Search for Video Action Se...

引用

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

作者： Gao, Shang-Hua Han, Qi Li, Zhong-Yu Peng, Pai Wang, Liang Cheng, Ming-Ming Nankai Univ TKLNDST CS Tianjin Peoples R China Tencent Shenzhen Peoples R China NLPR Beijing Peoples R China

ISBN: (纸本)9781665445092

Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation guided iterative local search scheme to refine combinations effectively. Our global-to-local search can be plugged into existing action segmentation methods to achieve state-of-the-art performance.

关键词： computer vision Codes Probabilistic logic pattern recognition Task analysis Forecasting

来源：评论

学校读者我要写书评

暂无评论

Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and inter-modal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https://***/praveena2j/RJCMA.

关键词： Correlation coefficient Visualization Emotion recognition computer vision Computational modeling conferences Refining

来源：评论

学校读者我要写书评

暂无评论

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

引用

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

作者： Yao, Yazhou Sun, Zeren Zhang, Chuanyi Shen, Fumin Wu, Qi Zhang, Jian Tang, Zhenmin Nanjing Univ Sci & Technol Nanjing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Univ Adelaide Adelaide SA Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments have validated the superiority of our approach over existing stateof-the-art methods.

关键词： Training Degradation Deep learning computer vision Codes Robustness 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 << < 441 442 443 444 445 446 447 448 449 450 > >>

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：