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

3,310 篇 会议
3 篇 期刊文献

馆藏范围

3,313 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,936 篇 工学
- 1,843 篇 计算机科学与技术...
- 201 篇 软件工程
- 147 篇 机械工程
- 134 篇 光学工程
- 41 篇 生物工程
- 28 篇 信息与通信工程
- 18 篇 电气工程
- 13 篇 控制科学与工程
- 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
360 篇 医学
- 359 篇 临床医学
- 3 篇 基础医学(可授医学...
178 篇 理学
- 137 篇 物理学
- 42 篇 生物学
- 30 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
- 3 篇 系统科学
14 篇 管理学
- 7 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 农学
1 篇 经济学

主题

1,738 篇 computer vision
900 篇 training
802 篇 conferences
643 篇 pattern recognit...
482 篇 computational mo...
431 篇 computer archite...
430 篇 task analysis
426 篇 visualization
350 篇 feature extracti...
317 篇 semantics
308 篇 three-dimensiona...
243 篇 neural networks
227 篇 benchmark testin...
222 篇 cameras
183 篇 image segmentati...
177 篇 estimation
168 篇 deep learning
156 篇 measurement
154 篇 object detection
151 篇 data models

机构

40 篇 univ sci & techn...
31 篇 peng cheng lab p...
29 篇 swiss fed inst t...
29 篇 sensetime res pe...
27 篇 university of sc...
27 篇 zhejiang univ pe...
26 篇 univ chinese aca...
26 篇 swiss fed inst t...
25 篇 university of ch...
24 篇 tsinghua univ pe...
24 篇 univ chinese aca...
24 篇 nanyang technol ...
24 篇 sun yat sen univ...
22 篇 peng cheng labor...
20 篇 shanghai ai lab ...
20 篇 korea adv inst s...
19 篇 chinese univ hon...
19 篇 yonsei univ
18 篇 peking univ peop...
18 篇 tsinghua univers...

作者

65 篇 timofte radu
21 篇 loy chen change
18 篇 van gool luc
18 篇 radu timofte
16 篇 zha zheng-jun
14 篇 sun jian
12 篇 fan haoqiang
12 篇 chen wei-ting
12 篇 lei lei
12 篇 qiao yu
11 篇 zheng wei-shi
11 篇 zheng-jun zha
11 篇 liu shuaicheng
11 篇 qi tian
11 篇 luc van gool
11 篇 marcos v. conde
11 篇 liu shuai
11 篇 van de weijer jo...
10 篇 danelljan martin
10 篇 kim seon joo

语言

3,311 篇 英文
2 篇 其他

检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020"

共 3313 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：

Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

引用

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

作者： Xu, Zhenlin Zhu, Yi Deng, Siqi Mittal, Abhay Chen, Yanbei Wang, Manchen Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98109 USA Boson AI Santa Clara CA 95054 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350365474

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： Benchmarking Foundational Models vision and language models Zero-shot recognition

来源：评论

学校读者我要写书评

暂无评论

A stroke of genius: Predicting the next move in badminton

A stroke of genius: Predicting the next move in badminton

引用

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

作者： Ibh, Magnus Grasshof, Stella Hansen, Dan Witzner IT Univ Copenhagen Machine Learning Grp Copenhagen Denmark

ISBN: (纸本)9798350365474

This paper presents, RallyTemPose, a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position, skeleton poses, and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks, which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion, the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons. The code can be found at: This https url.

关键词： Action Forecasting computer vision Encoder-Decoder Skeleton-data Sports Application

来源：评论

学校读者我要写书评

暂无评论

Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

引用

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

作者： Zhang, Haoyu Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Norwegian Univ Sci & Technol Trondheim Norway Darmstadt Univ Appl Sci Darmstadt Germany

ISBN: (纸本)9798350365474

Face morphing attacks have posed severe threats to Face recognition Systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Face recognition Morphing Attack Detection

来源：评论

学校读者我要写书评

暂无评论

Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

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

作者： Kim, Taehoon Marsden, Mark Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Sala, Alessandra Kim, Seung Hwan LG AI Res Seoul South Korea Shutterstock New York NY USA

ISBN: (纸本)9798350365474

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： image captioning large-scale multimodal transformers vision-language zero-shot

来源：评论

学校读者我要写书评

暂无评论

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

引用

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

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Samsung Res China Nanjing Peoples R China Nankai Univ Tianjin Peoples R China

ISBN: (纸本)9798350365474

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： computer vision flare removal image restoration

来源：评论

学校读者我要写书评

暂无评论

SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

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

作者： Mehri, Faridoun Fayyaz, Mohsen Baghshah, Mahdieh Soleymani Pilehvar, Mohammad Taher Sharif Univ Technol Tehran Iran Univ Tehran Tehran Iran Cardiff Univ Cardiff Wales

ISBN: (纸本)9798350365474

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-CVPR-2024.

关键词： Explainable AI Forward Attention-Based Token Attribution Interpretability Neural Network Visualization vision Transformers White-Box Input Attribution Methods xAI

来源：评论

学校读者我要写书评

暂无评论

Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

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

作者： Sharif, S. M. A. Myrzabekov, Azamat Khujaev, Nodirkhuja Tsoy, Roman Kim, Seongwan Lee, Jaeho LG Sciencepk Seoul South Korea

ISBN: (纸本)9798350365474

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Edge Device Edge LLIE Edge vision Task Low-light Image Enhancement Optimized LLIE Quantization

来源：评论

学校读者我要写书评

暂无评论

Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

引用

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

作者： Cieslak, Mikolaj Govindarajan, Umabharathi Garcia, Alejandro Chandrashekar, Anuradha Haedrich, Torsten Mendoza-Drosik, Aleksander Michels, Dominik L. Pirk, Soeren Fu, Chia-Chun Palubicki, Wojciech GreenMatterAI Berlin Germany Blue River Technol Santa Clara CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tech Univ Darmstadt Darmstadt Germany Christian Albrecht Univ Kiel Kiel Germany Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350365474

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

引用

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

作者： Hu, Yueyu Gong, Ran Sun, Qi Wang, Yao NYU Tandon Sch Engn Brooklyn NY 11201 USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350365474

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

引用

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

作者： Lee, Jiahe Steven Hsu, Wynne Lee, Mong Li Natl Univ Singapore Inst Data Sci Singapore Singapore Natl Univ Singapore Ctr Trusted Internet & Community Singapore Singapore

ISBN: (纸本)9798350365474

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： image copy detection image retrieval

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 << < 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：