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

23,000 篇 会议
126 册 图书
92 篇 期刊文献

馆藏范围

23,217 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,622 篇 工学
- 11,107 篇 计算机科学与技术...
- 3,479 篇 软件工程
- 2,445 篇 机械工程
- 1,716 篇 光学工程
- 1,075 篇 电气工程
- 1,014 篇 控制科学与工程
- 784 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
- 107 篇 安全科学与工程
- 100 篇 测绘科学与技术
- 88 篇 建筑学
- 86 篇 交通运输工程
- 84 篇 土木工程
3,494 篇 医学
- 3,481 篇 临床医学
- 81 篇 基础医学(可授医学...
3,241 篇 理学
- 1,939 篇 物理学
- 1,640 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
3,966 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
1,205 篇 computer archite...
1,155 篇 semantics
1,147 篇 conferences
1,107 篇 layout
1,092 篇 computer science
1,087 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...
107 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...
80 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
61 篇 tsinghua univ pe...
60 篇 peking univ peop...
59 篇 univ oxford oxfo...

作者

81 篇 van gool luc
72 篇 timofte radu
64 篇 zhang lei
47 篇 luc van gool
40 篇 yang yi
40 篇 li stan z.
37 篇 loy chen change
34 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
30 篇 pascal fua
29 篇 darrell trevor
29 篇 li fei-fei
28 篇 li xin
28 篇 ying shan
27 篇 vasconcelos nuno

语言

23,137 篇 英文
52 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 23218 条记录，以下是1051-1060 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：

GRES: Generalized Referring Expression Segmentation

GRES: Generalized Referring Expression Segmentation

引用

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

作者： Liu, Chang Ding, Henghui Jiang, Xudong Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object described by a given language expression. Existing classic RES datasets and methods commonly support single-target expressions only, i.e., one expression refers to one target object. Multi-target and no-target expressions are not considered. This limits the usage of RES in practice. In this paper, we introduce a new benchmark called Generalized Referring Expression Segmentation (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects. Towards this, we construct the first largescale GRES dataset called gRefCOCO that contains multitarget, no-target, and single-target expressions. GRES and gRefCOCO are designed to be well-compatible with RES, facilitating extensive experiments to study the performance gap of the existing RES methods on the GRES task. In the experimental study, we find that one of the big challenges of GRES is complex relationship modeling. Based on this, we propose a region-based GRES baseline ReLA that adaptively divides the image into regions with subinstance clues, and explicitly models the region-region and region-language dependencies. The proposed approach ReLA achieves new state-of-the-art performance on the both newly proposed GRES and classic RES tasks. The proposed gRefCOCO dataset and method are available at https://***/GRES.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

Neural Video Compression with Diverse Contexts

Neural Video Compression with Diverse Contexts

引用

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

作者： Li, Jiahao Li, Bin Lu, Yan Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://***/microsoft/DCVC.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Representation Learning with Text-Driven Soft Masks

Multi-Modal Representation Learning with Text-Driven Soft Ma...

引用

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

作者： Park, Jaeyoo Han, Bohyung Seoul Natl Univ Comp Vis Lab ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350301298

We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant to a certain word in the corresponding caption, instead of completely removing them. Since our framework relies only on image-caption pairs with no fine-grained annotations, we identify the relevant regions to each word by computing the word-conditional visual attention using multi-modal encoder. Second, we encourage the model to focus more on hard but diverse examples by proposing a focal loss for the image-text contrastive learning (ITC) objective, which alleviates the inherent limitations of overfitting and bias issues. Last, we perform multi-modal data augmentations for self-supervised learning via mining various examples by masking texts and rendering distortions on images. We show that the combination of these three innovations is effective for learning a pretrained model, leading to outstanding performance on multiple vision-language downstream tasks.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

Spatial-temporal Concept based Explanation of 3D ConvNets

Spatial-temporal Concept based Explanation of 3D ConvNets

引用

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

作者： Ji, Ying Wang, Yu Kato, Jien Nagoya Univ Nagoya Aichi Japan Hitotsubashi Univ Kunitachi Tokyo Japan Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9798350301298

Convolutional neural networks (CNNs) have shown remarkable performance on various tasks. Despite its widespread adoption, the decision procedure of the network still lacks transparency and interpretability, making it difficult to enhance the performance further. Hence, there has been considerable interest in providing explanation and interpretability for CNNs over the last few years. Explainable artificial intelligence (XAI) investigates the relationship between input images or videos and output predictions. Recent studies have achieved outstanding success in explaining 2D image classification ConvNets. On the other hand, due to the high computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. And none of them are able to produce a high-level explanation. In this paper, we propose a STCE (Spatial-temporal Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented with high-level supervoxels, similar supervoxels are clustered as a concept, which is straightforward for human to understand;and (2) the interpreting framework calculates a score for each concept, which reflects its significance in the ConvNet decision procedure. Experiments on diverse 3D ConvNets demonstrate that our method can identify global concepts with different importance levels, allowing us to investigate the impact of the concepts on a target task, such as action recognition, in-depth. The source codes are publicly available at https://***/yingji425/STCE.

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

Gloss Attention for Gloss-free Sign Language Translation

Gloss Attention for Gloss-free Sign Language Translation

引用

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

作者： Yin, Aoxiong Zhong, Tianyun Tang, Li Jin, Weike Jin, Tao Zhao, Zhou Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Most sign language translation (SLT) methods to date require the use of gloss annotations to provide additional supervision information, however, the acquisition of gloss is not easy. To solve this problem, we first perform an analysis of existing models to confirm how gloss annotations make SLT easier. We find that it can provide two aspects of information for the model, 1) it can help the model implicitly learn the location of semantic boundaries in continuous sign language videos, 2) it can help the model understand the sign language video globally. We then propose gloss attention, which enables the model to keep its attention within video segments that have the same semantics locally, just as gloss helps existing models do. Furthermore, we transfer the knowledge of sentence-to-sentence similarity from the natural language model to our gloss attention SLT network (GASLT) to help it understand sign language videos at the sentence level. Experimental results on multiple large-scale sign language datasets show that our proposed GASLT model significantly outperforms existing methods. Our code is provided in https://***/YinAoXiong/GASLT.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

DINER: Disorder-Invariant Implicit Neural Representation

DINER: Disorder-Invariant Implicit Neural Representation

引用

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

作者： Xie, Shaowen Zhu, Hao Liu, Zhen Zhang, Qi Zhou, You Cao, Xun Ma, Zhan Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Tencent Co Al Lab Shenzhen 518054 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems. However, the capacity of INR is limited by the spectral bias in the network training. In this paper, we find that such a frequency-related problem could be largely solved by re-arranging the coordinates of the input signal, for which we propose the disorder-invariant implicit neural representation (DINER) by augmenting a hash-table to a traditional INR backbone. Given discrete signals sharing the same histogram of attributes and different arrangement orders, the hash-table could project the coordinates into the same distribution for which the mapped signal can be better modeled using the subsequent INR network, leading to significantly alleviated spectral bias. Experiments not only reveal the generalization of the DINER for different INR backbones (MLP vs. SIREN) and various tasks (image/video representation, phase retrieval, and refractive index recovery) but also show the superiority over the state-of-the-art algorithms both in quality and speed. Project page: https://***/DINER-website/

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Neumann Network with Recursive Kernels for Single Image Defocus Deblurring

Neumann Network with Recursive Kernels for Single Image Defo...

引用

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

作者： Quan, Yuhui Wu, Zicong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9798350301298

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Multiplicative Fourier Level of Detail

Multiplicative Fourier Level of Detail

引用

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

作者： Dou, Yishun Zheng, Zhong Jin, Qiaoqiao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme.

关键词： vision + graphics

来源：评论

学校读者我要写书评

暂无评论

Simulating Task-Free Continual Learning Streams from Existing Datasets

Simulating Task-Free Continual Learning Streams from Existin...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Chrysakis, Aristotelis Moens, Marie-Francine KU Leuven Department of Computer Science Leuven Belgium

ISBN: (纸本)9798350302493

Task-free continual learning is the subfield of machine learning that focuses on learning online from a stream whose distribution changes continuously over time. In contrast, previous works evaluate task-free continual learning using streams with distributions that change not continuously, but only at a few distinct points in time. In order to address the discrepancy between the definition and evaluation of task-free continual learning, we propose a principled algorithm that can permute any labeled dataset into a stream that is continuously nonstationary. We empirically show that the streams generated by our algorithm are less structured than the ones conventionally used in the literature. Moreover, we use our simulated task-free streams to benchmark multiple methods applicable to the task-free setting. We hope that our work will allow other researchers to better evaluate learning performance on continuously nonstationary streams. © 2023 ieee.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Beyond AUROC & co. for evaluating out-of-distribution detection performance

Beyond AUROC & co. for evaluating out-of-distribution detect...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Humblot-Renaux, Galadrielle Escalera, Sergio Moeslund, Thomas B. Aalborg University Visual Analysis and Perception Lab Denmark Universitat Autònoma de Barcelona Computer Vision Center Spain Universitat de Barcelona Dept. of Mathematics and Informatics Spain

ISBN: (纸本)9798350302493

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://***/glhr/beyond-auroc © 2023 ieee.

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500页 << < 102 103 104 105 106 107 108 109 110 111 > >>

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：