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

13,623 篇 工学
- 11,107 篇 计算机科学与技术...
- 3,479 篇 软件工程
- 2,444 篇 机械工程
- 1,717 篇 光学工程
- 1,076 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
3,493 篇 医学
- 3,480 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

10,187 篇 computer vision
3,967 篇 pattern recognit...
3,005 篇 training
2,007 篇 computational mo...
1,818 篇 visualization
1,816 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
1,093 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...
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

23,023 篇 英文
166 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1231-1240 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：

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

Shortcomings of Top-Down Randomization-Based Sanity Checks f...

引用

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

作者： Binder, Alexander Weber, Leander Lapuschkin, Sebastian Montavon, Gregoire Muller, Klaus-Robert Samek, Wojciech SIT Singapore IICT Cluster Singapore Singapore UiO Oslo Oslo Norway Fraunhofer HHI Berlin Germany FU Berlin Berlin Germany BIFOLD Berlin Berlin Germany TU Berlin Berlin Germany Korea Univ Seoul Seoul South Korea MPI Saarbrucken Saarbrucken Germany SUTD Singapore Singapore Singapore

ISBN: (纸本)9798350301298

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing can be overinterpreted if regarded as a primary criterion for selecting or discarding explanation methods. To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e.g. [20]). We identify limitations of model-randomization-based sanity checks for the purpose of evaluating explanations. Firstly, we show that uninformative attribution maps created with zero pixel-wise covariance easily achieve high scores in this type of checks. Secondly, we show that top-down model randomization preserves scales of forward pass activations with high probability. That is, channels with large activations have a high probility to contribute strongly to the output, even after randomization of the network on top of them. Hence, explanations after randomization can only be expected to differ to a certain extent. This explains the observed experimental gap. In summary, these results demonstrate the inadequacy of model-randomization-based sanity checks as a criterion to rank attribution methods.

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

VLPD: Context-Aware Pedestrian Detection via vision-Language Semantic Self-Supervision

VLPD: Context-Aware Pedestrian Detection via Vision-Language...

引用

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

作者： Liu, Mengyin Jiang, Jie Zhu, Chao Yin, Xu-Cheng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Tencent Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://***/lmy98129/VLPD.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

引用

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

作者： Bannur, Shruthi Hyland, Stephanie Liu, Qianchu Perez-Garcia, Fernando Ilse, Maximilian Castro, Daniel C. Boecking, Benedikt Sharma, Harshita Bouzid, Kenza Thieme, Anja Schwaighofer, Anton Wetscherek, Maria Lungren, Matthew P. Nori, Aditya Alvarez-Valle, Javier Oktay, Ozan Microsoft Hlth Futures Redmond WA 98052 USA

ISBN: (纸本)9798350301298

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

关键词： cell microscopy Medical and biological vision

来源：评论

学校读者我要写书评

暂无评论

Seeing What You Miss: vision-Language Pre-training with Semantic Completion Learning

Seeing What You Miss: Vision-Language Pre-training with Sema...

引用

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

作者： Ji, Yatai Tu, Rongcheng Jiang, Jie Kong, Weijie Cai, Chengfei Zhao, Wenzhe Wang, Hongfa Yang, Yujiu Liu, Wei Tsinghua Univ Beijing Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval.

关键词： Multi-modal learning

来源：评论

学校读者我要写书评

暂无评论

DepGraph: Towards Any Structural Pruning

DepGraph: Towards Any Structural Pruning

引用

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

作者： Fang, Gongfan Ma, Xinyin Song, Mingli Mi, Michael Bi Wang, Xinchao Natl Univ Singapore Singapore Singapore Zhejiang Univ Hangzhou Peoples R China Huawei Technol Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks. However, the parameter-grouping patterns vary widely across different models, making architecture-specific pruners, which rely on manually-designed grouping schemes, non-generalizable to new architectures. In this work, we study a highly-challenging yet barely-explored task, any structural pruning, to tackle general structural pruning of arbitrary architecture like CNNs, RNNs, GNNs and Transformers. The most prominent obstacle towards this goal lies in the structural coupling, which not only forces different layers to be pruned simultaneously, but also expects all removed parameters to be consistently unimportant, thereby avoiding structural issues and significant performance degradation after pruning. To address this problem, we propose a general and fully automatic method, Dependency Graph (DepGraph), to explicitly model the dependency between layers and comprehensively group coupled parameters for pruning. In this work, we extensively evaluate our method on several architectures and tasks, including ResNe(X)t, DenseNet, MobileNet and vision transformer for images, GAT for graph, DGCNN for 3D point cloud, alongside LSTM for language, and demonstrate that, even with a simple norm-based criterion, the proposed method consistently yields gratifying performances.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

RIFormer: Keep Your vision Backbone Effective But Removing Token Mixer

RIFormer: Keep Your Vision Backbone Effective But Removing T...

引用

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

作者： Wang, Jiahao Zhang, Songyang Liu, Yong Wu, Taiqiang Yang, Yujiu Liu, Xihui Chen, Kai Luo, Ping Lin, Dahua Shanghai AI Lab Shanghai Peoples R China Univ HongKong Hong Kong Peoples R China Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798350301298

This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design.

关键词： Deep learning architectures and techniques

来源：评论

学校读者我要写书评

暂无评论

Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient vision Transformers

Beyond Attentive Tokens: Incorporating Token Importance and ...

引用

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

作者： Long, Sifan Zhao, Zhen Pi, Jimin Wang, Shengsheng Wang, Jingdong Jilin Univ Coll Comp Sci & Technol Jilin Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Jilin Jilin Peoples R China Baidu VIS Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we de-couple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%.

关键词： Deep learning architectures and techniques

来源：评论

学校读者我要写书评

暂无评论

Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

引用

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

作者： Seizinger, Tim Conde, Marcos V. Kolmet, Manuel Bishop, Tom E. Timofte, Radu University of Würzburg Computer Vision Lab CAIDAS & IFI Germany Glass Imaging Inc. CA United States

ISBN: (纸本)9798350302493

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark. © 2023 ieee.

关键词： Color photography

来源：评论

学校读者我要写书评

暂无评论

ResFormer: Scaling ViTs with Multi-Resolution Training

ResFormer: Scaling ViTs with Multi-Resolution Training

引用

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

作者： Tian, Rui Wu, Zuxuan Dai, Qi Hu, Han Qiao, Yu Jiang, Yu-Gang Fudan Univ Sch CS Shanghai Key Lab Intell Info Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have achieved overwhelming success, yet they suffer from vulnerable resolution scalability, i.e., the performance drops drastically when presented with input resolutions that are unseen during training. We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resolution training for improved performance on a wide spectrum of, mostly unseen, testing resolutions. In particular, ResFormer operates on replicated images of different resolutions and enforces a scale consistency loss to engage interactive information across different scales. More importantly, to alternate among varying resolutions effectively, especially novel ones in testing, we propose a global-local positional embedding strategy that changes smoothly conditioned on input sizes. We conduct extensive experiments for image classification on ImageNet. The results provide strong quantitative evidence that ResFormer has promising scaling abilities towards a wide range of resolutions. For instance, ResFormer-B-MR achieves a Top-1 accuracy of 75.86% and 81.72% when evaluated on relatively low and high resolutions respectively (i.e., 96 and 640), which are 48% and 7.49% better than DeiT-B. We also demonstrate, moreover, ResFormer is flexible and can be easily extended to semantic segmentation, object detection and video action recognition.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

A New Dataset and Approach for Timestamp Supervised Action Segmentation Using Human Object Interaction

A New Dataset and Approach for Timestamp Supervised Action S...

引用

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

作者： Sayed, Saif Ghoddoosian, Reza Trivedi, Bhaskar Athitsos, Vassilis University of Texas Arlington United States

ISBN: (纸本)9798350302493

This paper focuses on leveraging Human Object Interaction (HOI) information to improve temporal action segmentation under timestamp supervision, where only one frame is annotated for each action segment. This information is obtained from an off-the-shelf pre-trained HOI detector, that requires no additional HOI-related annotations in our experimental datasets. Our approach generates pseudo labels by expanding the annotated timestamps into intervals and allows the system to exploit the spatio-temporal continuity of human interaction with an object to segment the video. We also propose the (3+1)Real-time Cooking (ReC)1 dataset as a realistic collection of videos from 30 participants cooking 15 breakfast items. Our dataset has three main properties: 1) to our knowledge, the first to offer synchronized third and first person videos, 2) it incorporates diverse actions and tasks, and 3) it consists of high resolution frames to detect fine-grained information. In our experiments we benchmark state-of-the-art segmentation methods under different levels of supervision on our dataset. We also quantitatively show the advantages of using HOI information, as our framework improves its baseline segmentation method on several challenging datasets with varying viewpoints, providing improvements of up to 10.9% and 5.3% in F1 score and frame-wise accuracy respectively. © 2023 ieee.

关键词： computer vision

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 << < 120 121 122 123 124 125 126 127 128 129 > >>

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：