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...
3,010 篇 training
2,002 篇 computational mo...
1,816 篇 cameras
1,814 篇 visualization
1,515 篇 feature extracti...
1,482 篇 shape
1,459 篇 three-dimensiona...
1,439 篇 image segmentati...
1,289 篇 robustness
1,203 篇 computer archite...
1,158 篇 semantics
1,148 篇 conferences
1,106 篇 layout
1,093 篇 computer science
1,088 篇 object detection
1,024 篇 benchmark testin...
967 篇 codes
921 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...
97 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...
68 篇 microsoft res as...
66 篇 alibaba grp peop...
64 篇 adobe research
60 篇 peking univ peop...
59 篇 univ oxford oxfo...
59 篇 tsinghua univ pe...

作者

81 篇 van gool luc
71 篇 timofte radu
64 篇 zhang lei
51 篇 luc van gool
41 篇 li stan z.
40 篇 yang yi
37 篇 loy chen change
35 篇 chen chen
33 篇 xiaoou tang
33 篇 qi tian
32 篇 liu yang
32 篇 pascal fua
31 篇 tian qi
31 篇 sun jian
30 篇 murino vittorio
29 篇 darrell trevor
28 篇 li xin
28 篇 li fei-fei
27 篇 vasconcelos nuno
27 篇 hanqing lu

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1101-1110 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：

You Are Catching My Attention: Are vision Transformers Bad Learners under Backdoor Attacks?

You Are Catching My Attention: Are Vision Transformers Bad L...

引用

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

作者： Yuan, Zenghui Zhou, Pan Zou, Kai Cheng, Yu Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China Protagolabs Inc Vienna Austria Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention mechanism. However, compared with CNNs, we find that this mechanism of capturing global information within patches makes ViTs more sensitive to patch-wise triggers. Under such observations, we delicately design a novel backdoor attack framework for ViTs, dubbed BadViT, which utilizes a universal patch-wise trigger to catch the model's attention from patches beneficial for classification to those with triggers, thereby manipulating the mechanism on which ViTs survive to confuse itself. Furthermore, we propose invisible variants of BadViT to increase the stealth of the attack by limiting the strength of the trigger perturbation. Through a large number of experiments, it is proved that BadViT is an efficient backdoor attack method against ViTs, which is less dependent on the number of poisons, with satisfactory convergence, and is transferable for downstream tasks. Furthermore, the risks inside of ViTs to backdoor attacks are also explored from the perspective of existing advanced defense schemes.

关键词： Machine learning (other than deep learning)

来源：评论

学校读者我要写书评

暂无评论

GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for vision-and-Language Navigation

GeoVLN: Learning Geometry-Enhanced Visual Representation wit...

引用

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

作者： Huo, Jingyang Sun, Qiang Jiang, Boyan Lin, Haitao Fu, Yanwei Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.

关键词： Multi-modal learning

来源：评论

学校读者我要写书评

暂无评论

Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Causally-Aware Intraoperative Imputation for Overall Surviva...

引用

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

作者： Li, Xiang Qian, Xuelin Liang, Litian Kong, Lingjie Dong, Qiaole Chen, Jiejun Liu, Dingxia Yao, Xiuzhong Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Zhongshan Hosp Dept Radiol Shanghai Peoples R China

ISBN: (纸本)9798350301298

Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising results on liver cancer dataset of 361 patients with long-term observations.

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

Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Regression or Classification? Reflection on BP prediction fr...

引用

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

作者： Schrumpf, Fabian Serdack, Paul Rudi Fuchs, Mirco Leipzig Univ Appl Sci Leipzig Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification methods. The latter assign PPG signals to predefined BP intervals that represent clinically relevant ranges. The former predict systolic (SBP) and diastolic (DBP) BP as continuous variables and are of particular interest to the research community. However, the reported accuracies of BP regression methods vary widely among publications with some authors even questioning the feasibility of PPG-based BP regression altogether. In our work, we compare BP regression and classification approaches. We argue that BP classification might provide diagnostic value that is equivalent to regression in many clinically relevant scenarios while being similar or even superior in terms of performance. We compare several established neural architectures using publicly available PPG data for SBP regression and classification with and without personalization using subject-specific data. We found that classification and regression models perform similar before personalization. However, after personalization, the accuracy of classification based methods outperformed regression approaches. We conclude that BP classification might be preferable over BP regression in certain scenarios where a coarser segmentation of the BP range is sufficient.

关键词： Heart rate Deep learning computer vision conferences Neural networks Estimation computer architecture

来源：评论

学校读者我要写书评

暂无评论

Rapid 3D Model Generation with Intuitive 3D Input

Rapid 3D Model Generation with Intuitive 3D Input

引用

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

作者： Chen, Tianrun Ding, Chaotao Zhang, Shangzhan Yu, Chunan Zang, Ying Li, Zejian Peng, Sida Sun, Lingyun Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Huzhou Univ Sch Informat Engn Huzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Moxin Huzhou Technol Co Ltd KOKONI3D Huzhou Peoples R China

ISBN: (纸本)9798350353006

With the emergence of AR/VR, 3D models are in tremendous demand. However, conventional 3D modeling with computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices, in addition to serving as effective display mediums, can offer a promising potential as an intuitive 3D model creation tool, especially with the assistance of AI generative models. Here, we propose Deep3DVRSketch, the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds, irrespective of the users' drawing abilities. We also contribute KO3D+, the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data, then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment, our approach achieves state-of- the-art performance in both model quality and fidelity with real-world input from novice users, and users can even draw and obtain very detailed geometric structures. In our user study, users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://***/Deep3DVRSketch.

关键词： 3D models 3D vision generative models

来源：评论

学校读者我要写书评

暂无评论

Investigating Neural Architectures by Synthetic Dataset Design

Investigating Neural Architectures by Synthetic Dataset Desi...

引用

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

作者： Courtois, Adrien Morel, Jean-Michel Arias, Pablo ENS Paris Saclay Ctr Borelli 4 Av Sci F-91190 Gif Sur Yvette France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent years have seen the emergence of many new neural network structures (architectures and layers). To solve a given task, a network requires a certain set of abilities reflected in its structure. The required abilities depend on each task. There is so far no systematic study of the real capacities of the proposed neural structures. The question of what each structure can and cannot achieve is only partially answered by its performance on common benchmarks. Indeed, natural data contain complex unknown statistical cues. It is therefore impossible to know what cues a given neural structure is taking advantage of in such data. In this work, we sketch a methodology to measure the effect of each structure on a network's ability, by designing ad hoc synthetic datasets. Each dataset is tailored to assess a given ability and is reduced to its simplest form: each input contains exactly the amount of information needed to solve the task. We illustrate our methodology by building three datasets to evaluate each of the three following network properties: a) the ability to link local cues to distant inferences, b) the translation covariance and c) the ability to group pixels with the same characteristics and share information among them. Using a first simplified depth estimation dataset, we pinpoint a serious nonlocal deficit of the U-Net. We then evaluate how to resolve this limitation by embedding its structure with nonlocal layers, which allow computing complex features with long-range dependencies. Using a second dataset, we compare different positional encoding methods and use the results to further improve the U-Net on the depth estimation task. The third introduced dataset serves to demonstrate the need for self-attention-like mechanisms for resolving more realistic depth estimation tasks.

关键词： computer vision Systematics conferences Neural networks Buildings Estimation computer architecture

来源：评论

学校读者我要写书评

暂无评论

RV-GAN: Recurrent GAN for Unconditional Video Generation

RV-GAN: Recurrent GAN for Unconditional Video Generation

引用

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Inst Technol Dept Comp Sci Engn Visualizat & Percept Lab Madras Tamil Nadu India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generative models aiming to generate content from noise have achieved high-fidelity synthesis for image data. However, obtaining comparable performance in the field of unconditional video generation still remains challenging. In this work, we propose a recurrent GAN architecture to model the high-dimensional video data distribution. Recurrent networks by design are able to generate complex, long sequences in an autoregressive fashion. However, the standard LSTM unit for videos (ConvLSTM) is not ideally suited for the task of unconditional video generation. Therefore, we propose a simple yet effective LSTM variant called as TransConv LSTM (TC-LSTM) by modulating the conventional ConvLSTM to have a transpose convolutional structure in input-to-state transitions. This enables the network to model both spatial and temporal relationships across layers simultaneously inside the TC-LSTM unit. TC-LSTM unit acts as a building block of our generator. Extensive quantitative and qualitative analysis shows that RV-GAN outperforms state-of-the-art methods by a significant margin on Moving MNIST, MUG, Weizmann and UCF101 datasets. Additionally, owing to the recurrent structure, our method is able to generate high-quality videos, up to 2 times longer (32 frames) than training videos at inference time. Further analysis confirms that the proposed architecture is generic and can be easily adapted to other tasks like class-conditional video synthesis and text-to-video synthesis.

关键词： Training Architecture computer architecture Generative adversarial networks Data models Hybrid power systems Generators

来源：评论

学校读者我要写书评

暂无评论

Unbalanced Optimal Transport: A Unified Framework for Object Detection

Unbalanced Optimal Transport: A Unified Framework for Object...

引用

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

作者： De Plaen, Henri De Plaen, Pierre-Francois Suykens, Johan A. K. Proesmans, Marc Tuytelaars, Tinne Van Gool, Luc Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Katholieke Univ Leuven ESAT PSI Leuven Belgium Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9798350301298

During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the closest ground truth box (mostly used in combination with anchors), or matching via the Hungarian algorithm (mostly used in anchor-free methods). Each of these strategies comes with its own properties, underlying losses, and heuristics. We show how Unbalanced Optimal Transport unifies these different approaches and opens a whole continuum of methods in between. This allows for a finer selection of the desired properties. Experimentally, we show that training an object detection model with Unbalanced Optimal Transport is able to reach the state-of-the-art both in terms of Average Precision and Average Recall as well as to provide a faster initial convergence. The approach is well suited for GPU implementation, which proves to be an advantage for large-scale models.

关键词： computer vision theory

来源：评论

学校读者我要写书评

暂无评论

GamutMLP: A Lightweight MLP for Color Loss Recovery

GamutMLP: A Lightweight MLP for Color Loss Recovery

引用

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

作者： Le, Hoang M. Price, Brian Cohen, Scott Brown, Michael S. York Univ Toronto ON Canada Adobe Res San Jose CA USA

ISBN: (纸本)9798350301298

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image-the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Mobile User Interface Element Detection Via Adaptively Prompt Tuning

Mobile User Interface Element Detection Via Adaptively Promp...

引用

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

作者： Gu, Zhangxuan Xu, Zhuoer Chen, Haoxing Lan, Jun Meng, Changhua Wang, Weiqiang Tiansuan Lab Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it contains additional OCR information, which describes its content and function but is often ignored. In this paper, we develop a new MUI element detection dataset named MUI-zh and propose an Adaptively Prompt Tuning (APT) module to take advantage of discriminating OCR information. APT is a lightweight and effective module to jointly optimize category prompts across different modalities. For every element, APT uniformly encodes its visual features and OCR descriptions to dynamically adjust the representation of frozen category prompts. We evaluate the effectiveness of our plug-and-play APT upon several existing CLIP-based detectors for both standard and open-vocabulary MUI element detection. Extensive experiments show that our method achieves considerable improvements on two datasets. The datasets is available at github. com/antmachineintelligence/MUI-zh.

关键词： language reasoning 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 << < 107 108 109 110 111 112 113 114 115 116 > >>

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：