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

11,885 篇 会议
5 篇 期刊文献

馆藏范围

11,890 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,059 篇 工学
- 7,617 篇 计算机科学与技术...
- 796 篇 机械工程
- 688 篇 电气工程
- 360 篇 软件工程
- 228 篇 控制科学与工程
- 40 篇 光学工程
- 19 篇 生物工程
- 17 篇 信息与通信工程
- 12 篇 生物医学工程（可授...
- 6 篇 电子科学与技术（可...
- 6 篇 建筑学
- 6 篇 交通运输工程
- 5 篇 仪器科学与技术
- 5 篇 化学工程与技术
- 5 篇 安全科学与工程
- 4 篇 土木工程
3,347 篇 医学
- 3,346 篇 临床医学
- 4 篇 基础医学(可授医学...
- 4 篇 公共卫生与预防医...
253 篇 理学
- 198 篇 系统科学
- 32 篇 物理学
- 21 篇 生物学
- 18 篇 数学
- 9 篇 统计学（可授理学、...
- 7 篇 化学
17 篇 管理学
- 12 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 5 篇 工商管理
3 篇 法学
- 3 篇 社会学
3 篇 教育学
- 3 篇 教育学
2 篇 农学
1 篇 经济学
1 篇 军事学

主题

5,633 篇 computer vision
2,668 篇 training
2,203 篇 pattern recognit...
1,747 篇 computational mo...
1,502 篇 visualization
1,360 篇 three-dimensiona...
1,074 篇 semantics
999 篇 benchmark testin...
986 篇 codes
959 篇 computer archite...
891 篇 deep learning
777 篇 conferences
754 篇 task analysis
700 篇 feature extracti...
561 篇 transformers
533 篇 face recognition
527 篇 neural networks
495 篇 object detection
490 篇 image segmentati...
468 篇 cameras

机构

174 篇 univ sci & techn...
145 篇 carnegie mellon ...
144 篇 univ chinese aca...
144 篇 tsinghua univ pe...
134 篇 chinese univ hon...
110 篇 zhejiang univ pe...
109 篇 peng cheng lab p...
99 篇 swiss fed inst t...
91 篇 tsinghua univers...
90 篇 shanghai ai lab ...
87 篇 sensetime res pe...
86 篇 shanghai jiao to...
83 篇 zhejiang univers...
82 篇 tech univ munich...
79 篇 university of sc...
79 篇 stanford univ st...
78 篇 univ hong kong p...
77 篇 australian natl ...
76 篇 alibaba grp peop...
75 篇 peng cheng labor...

作者

75 篇 timofte radu
64 篇 van gool luc
50 篇 zhang lei
43 篇 yang yi
37 篇 loy chen change
36 篇 tao dacheng
32 篇 zhou jie
31 篇 chen chen
30 篇 liu yang
30 篇 tian qi
29 篇 sun jian
29 篇 zha zheng-jun
28 篇 li xin
27 篇 qi tian
26 篇 vasconcelos nuno
25 篇 liu xiaoming
25 篇 darrell trevor
24 篇 zheng wei-shi
24 篇 luo ping
24 篇 ying shan

语言

11,863 篇 英文
26 篇 其他
1 篇 中文

检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是501-510 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：

Single-Model and Any-Modality for Video Object Tracking

Single-Model and Any-Modality for Video Object Tracking

引用

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

作者： Wu, Zongwei Zheng, Jilai Ren, Xiangxuan Vasluianu, Florin-Alexandru Ma, Chao Paudel, Danda Pani Luc Van Gool Timofte, Radu Univ Wurzburg Comp Vision Lab CAIDAS & IFI Wurzburg Germany Shanghai Jiao Tong Univ AI Inst Shanghai Peoples R China Sofia Univ INSAIT Sofia Bulgaria Swiss Fed Inst Technol CVL Zurich Switzerland

ISBN: (纸本)9798350353006

In the realm of video object tracking, auxiliary modalities such as depth, thermal, or event data have emerged as valuable assets to complement the RGB trackers. In practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications. However, a similar single-model unification for multimodality tracking presents several challenges. These challenges stem from the inherent heterogeneity of inputs - each with modality-specific representations, the scarcity of multi-modal datasets, and the absence of all the modalities at all times. In this work, we introduce Un-Track, a Unified Tracker of a single set of parameters for any modality. To handle any modality, our method learns their common latent space through low-rank factorization and reconstruction techniques. More importantly, we use only the RGBX pairs to learn the common latent space. This unique shared representation seamlessly binds all modalities together, enabling effective unification and accommodating any missing modality, all within a single transformer-based architecture. Our Un-Track achieves +8.1 absolute F-score gain, on the DepthTrack dataset, by introducing only +2.14 (over 21.50) GFLOPs with +6.6M (over 93M) parameters, through a simple yet efficient prompting strategy. Extensive comparisons on five benchmark datasets with different modalities show that Un-Track surpasses both SOTA unified trackers and modality-specific counterparts, validating our effectiveness and practicality. The source code is publicly available at https:// github. com/ Zongwei97/ UnTrack.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

引用

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

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

来源：评论

学校读者我要写书评

暂无评论

PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization

PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera S...

引用

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

作者： Cai, Yanlu Zhang, Weizhong Wu, Yuan Jin, Cheng Fudan Univ Shanghai Peoples R China Haina Lab Shanghai Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact, showcasing significant potential in real applications. However, due to the limited camera setting diversity in the available datasets, the inferred camera parameters are always simply hard-coded into the model during training and not adaptable to the input in inference, making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples, i.e., 2D-3D pose pairs, under massive new camera settings. Unfortunately, to prevent over-fitting the existing camera setting, the number of synthesized samples for each new camera setting should be comparable with that for the existing one, which multiplies the scale of training and even makes it computationally prohibitive. In this paper, we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely, we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm, which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features, its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach.

关键词： computer vision Multi-View Pose Estimation

来源：评论

学校读者我要写书评

暂无评论

Coarse or Fine? Recognising Action End States without Labels

Coarse or Fine? Recognising Action End States without Labels

引用

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

作者： Moltisanti, Davide Bilen, Hakan Sevilla-Lara, Laura Keller, Frank Univ Bath Bath Avon England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350365474

We focus on the problem of recognising the end state of an action in an image, which is critical for understanding what action is performed and in which manner. We study this focusing on the task of predicting the coarseness of a cut, i.e., deciding whether an object was cut "coarsely" or "finely". No dataset with these annotated end states is available, so we propose an augmentation method to synthesise training data. We apply this method to cutting actions extracted from an existing action recognition dataset. Our method is object agnostic, i.e., it presupposes the location of the object but not its identity. Starting from less than a hundred images of a whole object, we can generate several thousands images simulating visually diverse cuts of different coarseness. We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects. Results demonstrate that the model successfully recognises the end state of the cutting action despite the domain gap between training and testing, and that the model generalises well to unseen objects.

关键词： adverb recognition fine-grained recognition object end-state recognition

来源：评论

学校读者我要写书评

暂无评论

A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

引用

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

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

来源：评论

学校读者我要写书评

暂无评论

What, when, and where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions

What, when, and where? Self-Supervised Spatio-Temporal Groun...

引用

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

作者： Chen, Brian Shvetsova, Nina Rouditchenko, Andrew Kondermann, Daniel Thomas, Samuel Chang, Shih-Fu Feris, Rogerio Glass, James Kuehne, Hilde Columbia Univ New York NY 10027 USA Goethe Univ Frankfurt Frankfurt Germany Univ Bonn Bonn Germany MIT CSAIL Cambridge MA USA Qual Match GmbH Heidelberg Germany IBM Res AI Yorktown Hts NY USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350353006

Spatio-temporal grounding describes the task of localizing events in space and time, e.g., in video data, based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective, proposing a frame-work for spatio-temporal action grounding trained on loose video and subtitle supervision only, without human annotation. To this end, we combine local representation learning, which focuses on leveraging fine-grained spatial information, with a global representation encoding that captures higher-level representations and incorporates both in a joint approach. To evaluate this challenging task in a real-life setting, a new benchmark dataset is proposed, providing dense spatio-temporal grounding annotations in long, untrimmed, multi-action instructional videos for over 5K events. We evaluate the proposed approach and other methods on the proposed and standard downstream tasks, showing that our method improves over current baselines in various settings, including spatial, temporal, and untrimmed multi-action spatio-temporal grounding.

关键词： Grounding Multimodal learning Self-supervised learning Video understanding vision and language

来源：评论

学校读者我要写书评

暂无评论

SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction

SIFU: Side-view Conditioned Implicit Function for Real-world...

引用

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

作者： Zhang, Zechuan Yang, Zongxin Yang, Yi Zhejiang Univ CCAL ReLER Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements, accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images, along with predicting textures for unseen areas, remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response, we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction), a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline. SIFU employs a cross-attention mechanism within the transformer, using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness, especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments, SIFU surpasses SOTA methods in both geometry and texture reconstruction, showcasing enhanced robustness in com-lex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building, demonstrating its broad utility in real-world scenarios.

关键词： 3D reconstruction 3D vision Clothed human diffusion model single image to 3D

来源：评论

学校读者我要写书评

暂无评论

Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

Generate Like Experts: Multi-Stage Font Generation by Incorp...

引用

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

作者： Fu, Bin Yu, Fanghua Liu, Anran Wang, Zixuan Wen, Jie He, Junjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples, which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods, which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers, in this paper, we model font generation as a multi-stage generative process. Specifically, as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces, we are able to incorporate the font transfer process into these models. Based on this observation, we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image, and the font transfer stage subsequently transforms the source font to the target font. Finally, the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process, we construct our font generation framework, named MSD-Font, with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://***/fubinfb/MSD-Font

关键词： Diffusion Model Few-shot Font Generation Probabilistic Generative Model

来源：评论

学校读者我要写书评

暂无评论

Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

引用

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

作者： Min, Seongjae Yang, Junseok Lim, Sejoon Kookmin Univ Seoul South Korea

ISBN: (纸本)9798350365474

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Look, Listen, and Attack: Backdoor Attacks Against Video Action recognition

Look, Listen, and Attack: Backdoor Attacks Against Video Act...

引用

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

作者： Hammoud, Hasan Abed Al Kader Liu, Shuming Alkhrashi, Mohammed AlBalawi, Fahad Ghanem, Bernard KAUST Thuwal Saudi Arabia SDAIA Riyadh Saudi Arabia Taif Univ Taif Saudi Arabia

ISBN: (纸本)9798350365474

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

关键词： action recognition adversarial attacks backdoor backdoor attacks video 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500页 << < 47 48 49 50 51 52 53 54 55 56 > >>

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：