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

283 篇 会议
41 册 图书
3 篇 期刊文献

馆藏范围

327 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

171 篇 工学
- 152 篇 计算机科学与技术...
- 93 篇 软件工程
- 47 篇 光学工程
- 35 篇 信息与通信工程
- 28 篇 生物工程
- 22 篇 电气工程
- 16 篇 控制科学与工程
- 9 篇 电子科学与技术（可...
- 8 篇 化学工程与技术
- 8 篇 生物医学工程（可授...
- 6 篇 网络空间安全
- 5 篇 机械工程
- 5 篇 安全科学与工程
- 3 篇 仪器科学与技术
- 3 篇 材料科学与工程（可...
- 3 篇 建筑学
- 3 篇 农业工程
78 篇 理学
- 48 篇 物理学
- 30 篇 生物学
- 25 篇 数学
- 11 篇 统计学（可授理学、...
- 8 篇 化学
21 篇 医学
- 16 篇 临床医学
- 4 篇 公共卫生与预防医...
- 3 篇 基础医学(可授医学...
- 3 篇 特种医学
18 篇 管理学
- 10 篇 管理科学与工程(可...
- 9 篇 图书情报与档案管...
- 5 篇 工商管理
4 篇 农学
- 4 篇 作物学
2 篇 法学
2 篇 教育学

主题

37 篇 computer vision
25 篇 image processing...
21 篇 artificial intel...
20 篇 pattern recognit...
20 篇 computer imaging...
17 篇 machine learning
14 篇 computer applica...
13 篇 computer systems...
12 篇 signal, image an...
11 篇 deep learning
8 篇 image processing
6 篇 computers and ed...
5 篇 vision transform...
5 篇 low-level vision
4 篇 image enhancemen...
4 篇 object detection
4 篇 cell microscopy
4 篇 image segmentati...
4 篇 graphics process...
4 篇 stereo image pro...

机构

17 篇 microsoft resear...
17 篇 microsoft res as...
12 篇 tsinghua univers...
7 篇 university of sc...
7 篇 univ sci & techn...
6 篇 national key lab...
6 篇 shanghai collabo...
6 篇 shanghai key lab...
5 篇 university of te...
5 篇 dalian universit...
5 篇 university of sy...
5 篇 institute of aut...
5 篇 shenzhen univers...
4 篇 key laboratory o...
4 篇 national enginee...
4 篇 university of ud...
4 篇 tsinghua univ pe...
4 篇 microsoft cloud ...
4 篇 university of yo...
4 篇 vision and image...

作者

8 篇 han hu
7 篇 hu han
6 篇 zuxuan wu
6 篇 min xu
5 篇 hui huang
5 篇 jing dong
5 篇 yu-gang jiang
5 篇 jiwen lu
5 篇 risheng liu
5 篇 wanli ouyang
5 篇 huchuan lu
4 篇 gian luca forest...
4 篇 qi dai
4 篇 zhiguo cao
4 篇 dongdong chen
4 篇 edwin hancock
4 篇 boxin shi
4 篇 jean-jacques rou...
4 篇 zheng zhang
4 篇 andrea fusiello

语言

321 篇 英文
6 篇 其他
3 篇 中文

检索条件"任意字段=2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition, CVIPPR 2023"

共 327 条记录，以下是21-30 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：

Deep Object Detection for Complex Architectural Floor Plans with Efficient Receptive Fields 23

Deep Object Detection for Complex Architectural Floor Plans ...

引用

2023 asia conference on computer vision, image processing and pattern recognition, cvippr 2023

作者： Xu, Zhongguo Jha, Naresh Mehadi, Syed Mandal, Mrinal Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Department of Medicine University of Alberta EdmontonAB Canada Clinisys Emr Inc. EdmontonAB Canada

ISBN: (纸本)9798400700033

Architectural floor plans play an important role in sharing the building information among engineers, designers, and clients. Automatic floor plan analysis can help in improving work efficiency and accuracy. Object detection and recognition are critical in understanding and analyzing a floor plan document. However, few research works have been conducted to date for automatic object detection in architectural floor plans. In this paper, a convolutional neural network, namely ArchNet, is proposed to detect various visual objects, such as door, window, and stairs. The ArchNet is a modified version of YOLO network, and consists of five modules: backbone, multiscale receptive fields, neck, head, and non-maximal suppression. In this paper, ArchNet is used to detect 13 object classes commonly found in architectural floor plans. Experimental results show that the proposed architecture can achieve a mean average precision of 75% which is superior compared to the state-of-the-art techniques. © 2023 ACM.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Skeleton-based Generative Adversarial Networks for Font Shape Style Transfer: Learning text style from some characters and transferring the style to any unseen characters 23

Skeleton-based Generative Adversarial Networks for Font Shap...

引用

2023 asia conference on computer vision, image processing and pattern recognition, cvippr 2023

作者： Thanusan, Thanaphon Patanukhom, Karn Graduate School Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9798400700033

This paper presents a new font shape style transfer technique that employs a generative adversarial network (GAN) and skeleton-based input feature maps to modify a target text to match a target font shape while retaining the original text content. Our GAN model is modified from a Shape-Matching GAN which utilizes a StyleNet generator and a PatchGAN discriminator. Rather than using a base-font character images as input to the generator like other existing font transfer models, we utilize the proposed skeleton-based features as input. The experimental results show that our model can produce the unseen characters in the desired font style better than an existing method. © 2023 ACM.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

NCVPRIPG 2023 Summer Challenge on Writer Verification 8th

NCVPRIPG 2023 Summer Challenge on Writer Verification

引用

8th National conference on computer vision, pattern recognition, image processing and Graphics-NCVPRIPG

作者： Prabhat, Gyan Shahid, Abu Gatti, Prajwal Shah, Manan Chutia, Girin Sharma, Arvind Kumar Mishra, Anand Indian Inst Technol Jodhpur Jodhpur Rajasthan India Cashfree Payments India Pvt Ltd Bengaluru India HCL Technol Ltd Chennai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

This report presents the outcomes of the Summer Challenge on Writer Verification, hosted as part of the Eighth National conference on computer vision, pattern recognition, image processing, and Graphics (NCVPRIPG) held at IIT Jodhpur on July 21-23, 2023. This challenge introduces a novel dataset comprising images of handwritten text contributed by 1,352 unique writers. Predominantly, these images feature handwritten Hindi text, but they also encompass a variety of elements such as numbers, mathematical symbols, and English text. Participants were tasked with developing a model capable of automatically determining whether the text in a given pair of images is authored by the same writer or different writers. The primary objective of this challenge was to advance research in the realm of handwritten text recognition. Throughout the competition, we registered 108 teams, with 18 teams submitting results for the validation dataset. Out of these, 13 teams provided submissions for the semi-final dataset. The top six teams from the semi-finals were subsequently invited to compete in the finals. Additional details about the challenge can be found at https://***/challenges/wv2023/.

关键词： Writer verification Handwritten text Indic language Document analysis Handwritten text recognition

来源：评论

学校读者我要写书评

暂无评论

iCLIP: Bridging image Classification and Contrastive Language-image Pre-training for Visual recognition

iCLIP: Bridging Image Classification and Contrastive Languag...

引用

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

作者： Wei, Yixuan Cao, Yue Zhang, Zheng Peng, Houwen Yao, Zhuliang Xie, Zhenda Hue, Han Guo, Baining Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naive multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and meanwhile surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https: //***/weiyx16/iCLIP.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

ParamISP: Learned Forward and Inverse ISPs using Camera Para...

引用

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

作者： Kim, Woohyeok Kim, Geonu Lee, Junyong Lee, Seungyong Baek, Seung-Hwan Cho, Sunghyun POSTECH Pohang South Korea Samsung AI Ctr Toronto Toronto ON Canada Samsung Toronto ON Canada

ISBN: (纸本)9798350353006

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated, enabling physically-meaningful RAW-level image processing on input sRGB images. However, existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time, and have limitations when used for various applications. In this paper, we propose ParamISP, a learning-based method for forward and inverse conversion between sRGB and RAW images, that adopts a novel neural-network module to utilize camera parameters, which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data, ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAWand sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis, raw deblurring, HDR reconstruction, and camera-to-camera transfer.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Self Attention in U-Net for Semantic Segmentation of Low Resolution SAR images 23

Self Attention in U-Net for Semantic Segmentation of Low Res...

引用

2023 asia conference on computer vision, image processing and pattern recognition, cvippr 2023

作者： Yadav, Hrishikesh Singh Panchal, Priyanshu Manawat, Divyanshu Girisha, S. Shreesha, S. Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798400700033

The SAR image semantic segmentation using computer vision techniques has gained much popularity in the research community due to their wide applications. Despite the advancements in Deep Learning for image analysis, these models still struggle to segment SAR images due to the existence of speckle noise and a poor feature extractor. Moreover, deep learning models are challenging to train on small datasets and the performance of the model is significantly impacted by the quality of the data. This calls for the development of an effective network that can draw out critical information from the low resolution SAR images. In this regard, the present work proposes a unique Self attention module in U-Net for the semantic segmentation of low resolution SAR images.. The Self Attention Model makes use of Laplacian kernel to highlight the sharp discontinuities in the features that define the boundaries of the objects. The proposed model, employs dilated convolution layers at the initial layers, enabling the model to more effectively capture larger contextual information. With an accuracy of 0.84 and an F1-score of 0.83, the proposed model outperforms the state-of-the-art techniques in semantic segmentation of low resolution SAR images. The results clearly demonstrate the importance of the self attention module and the consideration of dilated convolution layers in the initial layers in semantic segmentation of low resolution SAR images. © 2023 ACM.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Visual Programming: Compositional visual reasoning without training

Visual Programming: Compositional visual reasoning without t...

引用

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

作者： Gupta, Tanmay Kembhavi, Aniruddha PRIOR Allen Inst AI Seattle WA 98103 USA

ISBN: (纸本)9798350301298

We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing subroutines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform.

关键词： and reasoning language vision

来源：评论

学校读者我要写书评

暂无评论

Paint by Example: Exemplar-based image Editing with Diffusion Models

Paint by Example: Exemplar-based Image Editing with Diffusio...

引用

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

作者： Yang, Binxin Gu, Shuyang Zhang, Bo Zhang, Ting Chen, Xuejin Sun, Xiaoyan Chen, Dong Wen, Fang Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Redmond WA USA

ISBN: (纸本)9798350301298

Language-guided image editing has achieved great success recently. In this paper, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifacts. We carefully analyze it and propose a content bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without any iterative optimization. We demonstrate that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. The code and pretrained models are available at https://***/Fantasy-Studio/Paint-by-Example.

关键词： image and video synthesis and generation

来源：评论

学校读者我要写书评

暂无评论

ViTs for SITS: vision Transformers for Satellite image Time Series

ViTs for SITS: Vision Transformers for Satellite Image Time ...

引用

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

作者： Tarasiou, Michail Chavez, Erik Zafeiriou, Stefanos Imperial Coll London London England

ISBN: (纸本)9798350301298

In this paper we introduce the Temporo-Spatial vision Transformer (TSViT), a fully-attentional model for general Satellite image Time Series (SITS) processing based on the vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://***/michaeltrs/DeepSatModels.

关键词： Photogrammetry and remote sensing

来源：评论

学校读者我要写书评

暂无评论

Unifying vision, Text, and Layout for Universal Document processing

Unifying Vision, Text, and Layout for Universal Document Pro...

引用

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

作者： Tang, Zineng Yang, Ziyi Wang, Guoxin Fang, Yuwei Liu, Yang Zhu, Chenguang Zeng, Michael Zhang, Cha Bansal, Mohit Univ North Carolina Chapel Hill Chapel Hill NC 27599 USA Microsoft Azure Cognit Serv Res Redmond WA 98052 USA Microsoft Azure Visual Document Intelligence Redmond WA USA

ISBN: (纸本)9798350301298

We propose Universal Document processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark.(1)

关键词： Document analysis and understanding

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33页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：