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

3,915 篇 会议
2 篇 期刊文献

馆藏范围

3,917 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,011 篇 工学
- 2,919 篇 计算机科学与技术...
- 216 篇 软件工程
- 141 篇 机械工程
- 133 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 25 篇 电气工程
- 17 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
1,559 篇 医学
- 1,558 篇 临床医学
- 3 篇 基础医学(可授医学...
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
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 教育学
2 篇 农学
1 篇 经济学

主题

2,408 篇 computer vision
1,085 篇 training
1,043 篇 pattern recognit...
805 篇 conferences
709 篇 computational mo...
543 篇 visualization
491 篇 computer archite...
446 篇 three-dimensiona...
410 篇 semantics
409 篇 benchmark testin...
383 篇 codes
331 篇 transformers
290 篇 deep learning
277 篇 feature extracti...
260 篇 neural networks
256 篇 task analysis
238 篇 shape
216 篇 image segmentati...
204 篇 measurement
202 篇 object detection

机构

72 篇 tsinghua univ pe...
59 篇 univ sci & techn...
55 篇 zhejiang univ pe...
55 篇 chinese univ hon...
51 篇 carnegie mellon ...
51 篇 peng cheng lab p...
49 篇 swiss fed inst t...
47 篇 sensetime res pe...
46 篇 shanghai ai lab ...
42 篇 univ hong kong p...
38 篇 huawei noahs ark...
35 篇 univ chinese aca...
35 篇 shanghai jiao to...
33 篇 alibaba grp peop...
32 篇 tech univ munich...
31 篇 stanford univ st...
30 篇 peking univ peop...
30 篇 swiss fed inst t...
29 篇 adobe res san jo...
29 篇 google res mount...

作者

63 篇 timofte radu
30 篇 van gool luc
19 篇 yang yi
19 篇 qiao yu
18 篇 loy chen change
16 篇 zhang lei
16 篇 radu timofte
15 篇 sun jian
14 篇 liu yang
14 篇 liu shuaicheng
14 篇 tao dacheng
13 篇 li xin
13 篇 fan haoqiang
13 篇 chen wei-ting
12 篇 luo ping
12 篇 chen dongdong
12 篇 wang kai
12 篇 wang xinchao
12 篇 torralba antonio
12 篇 ghanem bernard

语言

3,916 篇 英文
1 篇 其他

检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3201-3210 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：

Semantic Pre-supplement for Exposure Correction

Semantic Pre-supplement for Exposure Correction

引用

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

作者： Zhen Zou Wei Yu Jie Huang Feng Zhao University of Science and Technology of China He Fei China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exposure correction tasks are dedicated to recovering the brightness and structural information of overexposed or underexposed images. The recovery difficulty of areas with different exposure levels is different, as severely exposed areas are more difficult to recover due to severe structural information loss than commonly exposed areas. However, existing methods focus on the simultaneous recovery of global brightness and structure, ignoring that the recovery difficulty varies between areas. To address this issue, we propose a novel exposure correction strategy named "Inpainting Assisted Exposure Correction"(IAEC), which pre-performs image structure repair on severely exposed areas to guide the exposure correction process. This method is based on the observation that the contextual semantic information contained in the image structure can effectively help the overall image recovery, and the lack of contextual semantic information in severely incorrectly exposed areas is very severe. The pre-performed structural repair by the inpainting model can well supplement the insufficient contextual semantic information caused by severe exposure. Therefore, we use an inpainting model to perform pre-structure repair on severely exposed areas to obtain supplementary contextual semantic information and then align the structure-repaired image with the improperly exposed input at the feature level. Extensive experiments demonstrate that our method gets superior results than the state-of-the-art methods and has the potential to be applied to other tasks with similar context loss problems.

关键词： Training computer vision conferences Semantics Brightness Maintenance engineering Cognition

来源：评论

学校读者我要写书评

暂无评论

How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

引用

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

作者： Tommie Kerssies Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16 × 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page 1 .

关键词： Training computer vision Analytical models Codes Semantic segmentation conferences Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

in2IN: Leveraging individual Information to Generate Human INteractions

in2IN: Leveraging individual Information to Generate Human I...

引用

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

作者： Pablo Ruiz-Ponce German Barquero Cristina Palmero Sergio Escalera José García-Rodríguez Universidad de Alicante San Vicente del Raspeig Spain Universitat de Barcelona Barcelona Spain Computer Vision Center Cerdanyola del Vallès Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse. Alongside this utility also comes a great diffi-culty in modeling the highly dimensional inter-personal dynamics. In addition, properly capturing the intra-personal diversity of interactions has a lot of challenges. Current methods generate interactions with limited diversity of intra-person dynamics due to the limitations of the available datasets and conditioning strategies. For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction. To train this model, we use a large language model to extend the InterHuman dataset with individual descriptions. As a result, in2IN achieves state-of-the- art performance in the InterHuman dataset. Furthermore, in order to increase the intra-personal diversity on the existing interaction datasets, we propose DualMDM, a model composition technique that combines the motions generated with in2IN and the motions generated by a single-person motion prior pre-trained on HumanML3D. As a result, DualMDM generates motions with higher individual diversity and improves control over the intra-person dynamics while maintaining inter-personal coherence.

关键词： Legged locomotion computer vision Metaverse Large language models conferences Dynamics Diversity reception

来源：评论

学校读者我要写书评

暂无评论

AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements

AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot...

引用

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

作者： Calvin Yeung Kenjiro Ide Keisuke Fujii Nagoya University Japan RIKEN/JST PRESTO Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image understanding is a foundational task in computer vision, with recent applications emerging in soccer posture analysis. However, existing publicly available datasets lack comprehensive information, notably in the form of posture sequences and 2D pose annotations. Moreover, current analysis models often rely on interpretable linear models (e.g., PCA and regression), limiting their capacity to capture non-linear spatiotemporal relationships in complex and diverse scenarios. To address these gaps, we introduce the 3D Shot Posture (3DSP) dataset in soccer broadcast videos, which represents the most extensive sports image dataset with 2D pose annotations to our knowledge. Additionally, we present the 3DSP-GRAE (Graph Recurrent AutoEncoder) model, a non-linear approach for embedding pose sequences. Furthermore, we propose AutoSoccerPose, a pipeline aimed at semi-automating 2D and 3D pose estimation and posture analysis. While achieving full automation proved challenging, we provide a foundational baseline, extending its utility beyond the scope of annotated data. We validate AutoSoccerPose on SoccerNet and 3DSP datasets, and present posture analysis results based on 3DSP. The dataset, code, and models are available at: https://***/calvinyeungck/3D-Shot-Posture-Dataset.

关键词： computer vision Analytical models Three-dimensional displays Annotations Computational modeling Pose estimation Spatiotemporal phenomena

来源：评论

学校读者我要写书评

暂无评论

SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations

SciFlow: Empowering Lightweight Optical Flow Models with Sel...

引用

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

作者： Jamie Menjay Lin Jisoo Jeong Hong Cai Risheek Garrepalli Kai Wang Fatih Porikli Qualcomm Technologies Inc. Qualcomm AI Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex challenge. First, an optical flow model must be sufficiently lightweight to meet computation and memory constraints to ensure real-time performance on devices. Second, the necessity for real-time on-device operation imposes constraints that weaken the model’s capacity to adequately handle ambiguities in flow estimation, thereby intensifying the difficulty of preserving flow *** paper introduces two synergistic techniques, Self-Cleaning Iteration (SCI) and Regression Focal Loss (RFL), designed to enhance the capabilities of optical flow models, with a focus on addressing optical flow regression ambiguities. These techniques prove particularly effective in mitigating error propagation, a prevalent issue in optical flow models that employ iterative refinement. Notably, these techniques add negligible to zero overhead in model parameters and inference latency, thereby preserving real-time on-device *** effectiveness of our proposed SCI and RFL techniques, collectively referred to as SciFlow for brevity, is demonstrated across two distinct lightweight optical flow model architectures in our experiments. Remarkably, SciFlow enables substantial reduction in error metrics (EPE and Fl-all) over the baseline models by up to 6.3% and 10.5% for in-domain scenarios and by up to 6.2% and 13.5% for cross-domain scenarios on the Sintel and KITTI 2015 datasets, respectively.

关键词： Optical losses Training Performance evaluation computer vision Image motion analysis Computational modeling Estimation

来源：评论

学校读者我要写书评

暂无评论

Improving the Robustness of 3D Human Pose Estimation: A Benchmark Dataset and Learning from Noisy Input

Improving the Robustness of 3D Human Pose Estimation: A Benc...

引用

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

作者： Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do University of Illinois at Urbana-Champaign USA VinUniversity Ha Noi Vietnam The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their robustness on challenging in-the-wild videos remain an open problem. In this work, we focus on building robust 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the resilience of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We demonstrate the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research.

关键词： Solid modeling Three-dimensional displays Convolution Pose estimation Benchmark testing Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

引用

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

作者： Farzaneh Askari Cyril Yared Rohit Ramaprasad Devin Garg Anjun Hu James J. Clark University of McGill Montreal QC Canada University of California San Diego San Diego CA United States University of Oxford Oxford United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Adaptation models computer architecture Transformers Skeleton Graph neural networks Data models Recording

来源：评论

学校读者我要写书评

暂无评论

Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

引用

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

作者： Zimeng Liu Bin Huang Chun-Liang Lin Chieh-Liang Wu Changchen Zhao Wen-Cheng Chao Yu-Cheng Wu Yadan Zheng Zhiru Wang Hangzhou Innovataion Institute Beihang University School of Automation Science and Electrical Engineering Beihang University College of Electrical Engineering and Computer Science National Chung Hsing University Department of Critical Care Medicine Taichung Veterans General Hospital Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Big Data Center National Chung Hsing University Computer Science School Beijing University of Posts and Telecommunications Graduate School of Translation and Interpretation Beijing Foreign Studies University

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting

Parameter Efficient Fine-tuning of Self-supervised ViTs with...

引用

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

作者： Reza Akbarian Bafghi Nidhin Harilal Claire Monteleoni Maziar Raissi University of Colorado Boulder INRIA Paris University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge. We observe that this issue is particularly magnified in vision transformers (ViTs), where post-pre-training and fine-tuning on new tasks can significantly degrade the model’s original general abilities. For instance, a DINO ViT-Base/16 pre-trained on ImageNet-1k loses over 70% accuracy on ImageNet-1k after just 10 iterations of fine-tuning on CIFAR-100. Overcoming this stability-plasticity dilemma is crucial for enabling ViTs to continuously learn and adapt to new domains while preserving their initial knowledge. In this work, we study two new parameter-efficient fine-tuning strategies: (1) Block Expansion, and (2) Low-rank adaptation (LoRA). Our experiments reveal that using either Block Expansion or LoRA on self-supervised pre-trained ViTs surpass fully fine-tuned ViTs in new domains while offering significantly greater parameter efficiency. Notably, we find that Block Expansion experiences only a minimal performance drop in the pre-training domain, thereby effectively mitigating catastrophic forgetting in pre-trained ViTs 1 .

关键词： Knowledge engineering computer vision Adaptation models conferences Learning (artificial intelligence) Artificial neural networks Transformers

来源：评论

学校读者我要写书评

暂无评论

Swift Parameter-free Attention Network for Efficient Super-Resolution

Swift Parameter-free Attention Network for Efficient Super-R...

引用

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

作者： Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zuo Georgia Institute of Technology Xiaomi Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional attention mechanisms have significantly improved SISR performance but often result in complex network structures and large number of parameters, leading to slow inference speed and large model size. To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality. SPAN employs a novel parameter-free attention mechanism, which leverages symmetric activation functions and residual connections to enhance high-contribution information and suppress redundant information. Our theoretical analysis demonstrates the effectiveness of this design in achieving the attention mechanism’s purpose. We evaluate SPAN on multiple benchmarks, showing that it outperforms existing efficient super-resolution models in terms of both image quality and inference speed, achieving a significant quality-speed trade-off. This makes SPAN highly suitable for real-world applications, particularly in resource-constrained scenarios. Notably, we won the first place both in the overall performance track and runtime track of the NTIRE 2024 efficient super-resolution challenge. Our code and models are made publicly available at https://***/hongyuanyu/span.

关键词： Image quality Performance evaluation computer vision Attention mechanisms Runtime Computational modeling Superresolution

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392页 << < 317 318 319 320 321 322 323 324 325 326 > >>

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：