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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
- 2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,319 篇 英文
1 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2391-2400 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：

DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging

DRHDR: A Dual branch Residual Network for Multi-Bracket High...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Department of Mathematics and Computer Science (IMADA) University of Southern Denmark Esoft Systems

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.

关键词： computer vision conferences Imaging Dynamic range pattern recognition Complexity theory Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

AutoRecon: Automated 3D Object Discovery and Reconstruction

AutoRecon: Automated 3D Object Discovery and Reconstruction

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yuang Wang Xingyi He Sida Peng Haotong Lin Hujun Bao Xiaowei Zhou State Key Lab of CAD&CG Zhejiang University ZJU-SenseTime Joint Lab of 3D Vision

A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we propose a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. We demonstrate that foreground objects can be robustly located and segmented from SfM point clouds by leveraging self-supervised 2D vision transformer features. Then, we reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the effectiveness and robustness of AutoRecon. The code and supplementary material are available on the project page: https://***/autorecon/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning Expressive Prompting With Residuals for vision Transformers

Learning Expressive Prompting With Residuals for Vision Tran...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Rajshekhar Das Yonatan Dukler Avinash Ravichandran Ashwin Swaminathan Carnegie Mellon University AWS AI Labs

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Our method constructs downstream representations via learnable “output” tokens (shal-low prompts), that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introduce residual learnable tokens that are added to the output of various computations. We apply EXPRES for image classification and few-shot semantic segmentation, and show our method is capable of achieving state of the art prompt tuning on 3/3 categories of the VTAB benchmark. In addition to strong performance, we observe that our approach is an order of magnitude more prompt efficient than existing visual prompting baselines. We analytically show the computational benefits of our approach over weight space adaptation techniques like finetuning. Lastly we systematically corroborate the architectural design of our method via a series of ablation experiments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring Structured Semantic Prior for Multi Label recognition with Incomplete Labels

Exploring Structured Semantic Prior for Multi Label Recognit...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zixuan Ding Ao Wang Hui Chen Qiang Zhang Pengzhang Liu Yongjun Bao Weipeng Yan Jungong Han Xidian University Hangzhou Zhuoxi Institute of Brain and Intelligence Tsinghua University BNRist *** Department of Computer Science The University of Sheffield UK Centre for Machine Intelligence the University of Sheffield UK

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label corre-spondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCP-Net), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly out-performs existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://***/jameslahm/SCPNet.

关键词：

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

conference on computer vision and pattern recognition (CVPR)

作者： Henri De Plaen Pierre-François De Plaen Johan A. K. Suykens Marc Proesmans Tinne Tuytelaars Luc Van Gool ESAT-STADIUS KU Leuven Belgium ESAT-PSI KU Leuven Belgium Computer Vision Lab ETH Zürich Switzerland

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：

来源：评论

学校读者我要写书评

暂无评论

CPARR: Category-based Proposal Analysis for Referring Relationships

CPARR: Category-based Proposal Analysis for Referring Relati...

引用

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

作者： He, Chuanzi Zhu, Haidong Gao, Jiyang Chen, Kan Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781728193601

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of . This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

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

作者： Gladisch, Christoph Heinzemann, Christian Herrmann, Martin Woehrle, Matthias Robert Bosch GmbH Corp Res Gerlingen Germany

ISBN: (纸本)9781728193601

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for vision Decoding

Seeing Beyond the Brain: Conditional Diffusion Model with Sp...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zijiao Chen Jiaxin Qing Tiange Xiang Wan Lin Yue Juan Helen Zhou National University of Singapore The Chinese University of Hong Kong Stanford University

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively; the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Video Test-Time Adaptation for Action recognition

Video Test-Time Adaptation for Action Recognition

引用

conference on computer vision and pattern recognition (CVPR)

作者： Wei Lin Muhammad Jehanzeb Mirza Mateusz Kozinski Horst Possegger Hilde Kuehne Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Christian Doppler Laboratory for Embedded Machine Learning Goethe University Frankfurt Germany MIT-IBM Watson AI Lab

Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data. However, test-time adaptation of video action recognition models against common distribution shifts has so far not been demonstrated. We propose to address this problem with an approach tailored to spatio-temporal models that is capable of adaptation on a single video sample at a step. It consists in a feature distribution alignment technique that aligns online estimates of test set statistics towards the training statistics. We further enforce prediction consistency over temporally augmented views of the same test video sample. Evaluations on three benchmark action recognition datasets show that our proposed technique is architecture-agnostic and able to significantly boost the performance on both, the state of the art convolutional architecture TANet and the Video Swin Transformer. Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts. Code will be available at https://***/wlin-at/ViTTA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Robustness of vision Transformers by Reducing Sensitivity to Patch Corruptions

Improving Robustness of Vision Transformers by Reducing Sens...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yong Guo David Stutz Bernt Schiele Max Planck Institute for Informatics Saarland Informatics Campus

Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensitive to the corruptions across patches. For example, when we only occlude a small number of patches with random noise (e.g., 10%), these patch corruptions would lead to severe accuracy drops and greatly distract intermediate attention layers. To address this, we propose a new training method that improves the robustness of transformers from a new perspective - reducing sensitivity to patch corruptions (RSPC). Specifically, we first identify and occlude/corrupt the most vulnerable patches and then explicitly reduce sensitivity to them by aligning the intermediate features between clean and corrupted examples. We highlight that the construction of patch corruptions is learned adversarially to the following feature alignment process, which is particularly effective and essentially different from existing methods. In experiments, our RSPC greatly improves the stability of attention layers and consistently yields better robustness on various benchmarks, including CIFAR-10/100-C, ImageNet-A, ImageNet-C, and ImageNet-P.

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332页 << < 236 237 238 239 240 241 242 243 244 245 > >>

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：