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

11,892 篇 会议
5 篇 期刊文献

馆藏范围

11,897 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,066 篇 工学
- 7,624 篇 计算机科学与技术...
- 796 篇 机械工程
- 690 篇 电气工程
- 366 篇 软件工程
- 233 篇 控制科学与工程
- 41 篇 光学工程
- 22 篇 信息与通信工程
- 19 篇 生物工程
- 12 篇 生物医学工程（可授...
- 9 篇 交通运输工程
- 8 篇 电子科学与技术（可...
- 7 篇 安全科学与工程
- 6 篇 仪器科学与技术
- 6 篇 建筑学
- 5 篇 化学工程与技术
- 5 篇 网络空间安全
- 4 篇 土木工程
3,348 篇 医学
- 3,347 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...
- 4 篇 特种医学
259 篇 理学
- 198 篇 系统科学
- 34 篇 物理学
- 22 篇 数学
- 21 篇 生物学
- 9 篇 化学
- 9 篇 统计学（可授理学、...
19 篇 管理学
- 14 篇 管理科学与工程(可...
- 8 篇 图书情报与档案管...
- 5 篇 工商管理
3 篇 法学
3 篇 教育学
2 篇 农学
1 篇 经济学
1 篇 军事学

主题

5,634 篇 computer vision
2,668 篇 training
2,206 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...
892 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

11,804 篇 英文
92 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 11897 条记录，以下是1711-1720 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：

ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

ReConPatch : Contrastive Patch Representation Learning for I...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hyun, Jeeho Kim, Sangyun Jeon, Giyoung Kim, Seung Hwan Bae, Kyunghoon Kang, Byung Jun LG AI Res Bundang Dong South Korea

ISBN: (纸本)9798350318920;9798350318937

Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations pre-trained from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmentation which should be carefully designed, particularly for the industrial dataset. In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model. ReConPatch employs contrastive representation learning to collect and distribute features in a way that produces a target-oriented and easily separable representation. To address the absence of labeled pairs for the contrastive learning, we utilize two similarity measures between data representations, pairwise and contextual similarities, as pseudo-labels. Our method achieves the state-of-the-art anomaly detection performance (99.72%) for the widely used and challenging MVTec AD dataset. Additionally, we achieved a state-of-the-art anomaly detection performance (95.8%) for the BTAD dataset.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

来源：评论

学校读者我要写书评

暂无评论

Generating High Fidelity Data from Low-density Regions using Diffusion Models

Generating High Fidelity Data from Low-density Regions using...

引用

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

作者： Sehwag, Vikash Hazirbas, Caner Gordo, Albert Ozgenel, Firat Ferrer, Cristian Canton Princeton Univ Princeton NJ 08544 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold. Therefore, we modify the sampling process to guide it towards low-density regions while simultaneously maintaining the fidelity of synthetic data. We rigorously demonstrate that our process successfully generates novel high fidelity samples from low-density regions. We further examine generated samples and show that the model does not memorize low-density data and indeed learns to generate novel samples from low-density regions.

关键词： Manifolds computer vision Computational modeling Diffusion processes Data models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection

EResFD: Rediscovery of the Effectiveness of Standard Convolu...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Jeong, Joonhyun Kim, Beomyoung Yu, Joonsang Yoo, YoungJoon NAVER Cloud ImageVis Seongnam South Korea Korea Adv Inst Sci & Technol Daejeon South Korea NAVER AI Lab Grenoble France

ISBN: (纸本)9798350318920;9798350318937

This paper analyzes the design choices of face detection architecture that improve efficiency of computation cost and accuracy. Specifically, we re-examine the effectiveness of the standard convolutional block as a lightweight backbone architecture for face detection. Unlike the current tendency of lightweight architecture design, which heavily utilizes depthwise separable convolution layers, we show that heavily channel-pruned standard convolution layers can achieve better accuracy and inference speed when using a similar parameter size. This observation is supported by the analyses concerning the characteristics of the target data domain, faces. Based on our observation, we propose to employ ResNet with a highly reduced channel, which surprisingly allows high efficiency compared to other mobilefriendly networks (e.g., MobileNetV1, V2, V3). From the extensive experiments, we show that the proposed backbone can replace that of the state-of-the-art face detector with a faster inference speed. Also, we further propose a new feature aggregation method to maximize the detection performance. Our proposed detector EResFD obtained 80.4% mAP on WIDER FACE Hard subset which only takes 37.7 ms for VGA image inference on CPU. Code is available at https://***/clovaai/EResFD.

关键词： Algorithms Image recognition and understanding

来源：评论

学校读者我要写书评

暂无评论

Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation

Aligning Non-Causal Factors for Transformer-Based Source-Fre...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Sanyal, Sunandini Asokan, Ashish Ramayee Bhambri, Suvaansh Pradyumna, Y. M. Kulkarni, Akshay Kundu, Jogendra Nath Babu, R. Venkatesh Indian Insitute Sci Vis & AI Lab Bengaluru India

ISBN: (纸本)9798350318920;9798350318937

Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain. However, we find that retaining the spurious correlation between causal and non-causal factors plays a vital role in bridging the domain gap and improving target adaptation. Therefore, we propose to build a framework that disentangles and supports causal factor alignment by aligning the non-causal factors first. We also investigate and find that the strong shape bias of vision transformers, coupled with its multi-head attentions, make it a suitable architecture for realizing our proposed disentanglement. Hence, we propose to build a Causality-enforcing Source-Free Transformer framework (C-SFTrans(1)) to achieve disentanglement via a novel two-stage alignment approach: a) non-causal factor alignment: non-causal factors are aligned using a style classification task which leads to an overall global alignment, b) task-discriminative causal factor alignment: causal factors are aligned via target adaptation. We are the first to investigate the role of vision transformers (ViTs) in a privacy-preserving source-free setting. Our approach achieves state-of-the-art results in several DA benchmarks.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

来源：评论

学校读者我要写书评

暂无评论

Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation

Generalization by Adaptation: Diffusion-Based Domain Extensi...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Niemeijer, Joshua Schwonberg, Manuel Termoehlen, Jan-Aike Schmidt, Nico M. Fingscheidt, Tim DLR Cologne Germany CARIAD SE Wolfsburg Germany Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9798350318920;9798350318937

When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods try to overcome this issue, but need samples from the target domain. However, this might not always be feasible for various reasons and therefore domain generalization methods are useful as they do not require any target data. We present a new diffusion-based domain extension (DIDEX) method and employ a diffusion model to generate a pseudo-target domain with diverse text prompts. In contrast to existing methods, this allows to control the style and content of the generated images and to introduce a high diversity. In a second step, we train a generalizing model by adapting towards this pseudo-target domain. We outperform previous approaches by a large margin across various datasets and architectures without using any real data. For the generalization from GTA5, we improve state-of-the-art mIoU performance by 3.8 % absolute on average and for SYNTHIA by 11.8 % absolute, marking a big step for the generalization performance on these benchmarks. Code is available at https://***/JNiemeijer/DIDEX

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

来源：评论

学校读者我要写书评

暂无评论

More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech

More than Words: In-the-Wild Visually-Driven Prosody for Tex...

引用

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

作者： Hassid, Michael Ramanovich, Michelle Tadmor Shillingford, Brendan Wang, Miaosen Jia, Ye Remez, Tal Google Res Mountain View CA 94043 USA DeepMind London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes ad-vantage of video frames as an additional input alongside text, and generates speech that matches the video signal. We demonstrate how this allows VDTTS to, unlike plain TTS models, generate speech that not only has prosodic variations like natural pauses and pitch, but is also synchronized to the input video. Experimentally, we show our model produces well-synchronized outputs, approaching the video-speech synchronization quality of the ground-truth, on several challenging benchmarks including "in-the-wild" content from VoxCeleb2. Supplementary demo videos demonstrating video-speech synchronization, robustness to speaker ID swapping, and prosody, presented at the project page.(1)

关键词： computer vision Machine learning Benchmark testing Robustness pattern recognition Synchronization

来源：评论

学校读者我要写书评

暂无评论

Fast and Interpretable Face Identification for Out-Of-Distribution Data Using vision Transformers

Fast and Interpretable Face Identification for Out-Of-Distri...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hai Phan Le, Cindy X. Le, Vu He, Yihui AnhTotti Nguyen Auburn Univ Auburn AL 36849 USA Columbia Univ New York NY USA Phenikaa Univ Hanoi Vietnam Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9798350318920;9798350318937

Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level. Yet, this technique can be subject to occlusion (e.g., faces with masks or sunglasses) and out-of-distribution data. DeepFace-EMD [40] reaches state-of-the-art accuracy on out-of-distribution data by first comparing two images at the image level, and then at the patch level. Yet, its later patch-wise re-ranking stage admits a large O(n(3) log n) time complexity (for n patches in an image) due to the optimal transport optimization. In this paper, we propose a novel, 2-image vision Transformers (ViTs) that compares two images at the patch level using cross attention. After training on 2M pairs of images on CASIA Webface [58], our model performs at a comparable accuracy as DeepFace-EMD on out-of-distribution data, yet at an inference speed more than twice as fast as DeepFace-EMD [40]. In addition, via a human study, our model shows promising explainability through the visualization of cross-attention. We believe our work can inspire more explorations in using ViTs for face identification.

关键词： Algorithms Algorithms Biometrics body pose face gesture Image recognition and understanding

来源：评论

学校读者我要写书评

暂无评论

What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection

What's in the Flow? Exploiting Temporal Motion Cues for Unsu...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Gothe, Sourabh Vasant Agarwal, Vibhav Ghosh, Sourav Vachhani, Jayesh Rajkumar Kashyap, Pranay Raja, Barath Raj Kandur Samsung R&D Inst Bangalore Bangalore Karnataka India

ISBN: (纸本)9798350318920;9798350318937

Generic Event Boundary Detection (GEBD) task aims to recognize generic, taxonomy-free boundaries that segment a video into meaningful events. Current methods typically involve a neural model trained on a large volume of data, demanding substantial computational power and storage space. We explore two pivotal questions pertaining to GEBD: Can non-parametric algorithms outperform unsupervised neural methods? Does motion information alone suffice for high performance? This inquiry drives us to algorithmically harness motion cues for identifying generic event boundaries in videos. In this work, we propose FlowGEBD, a non-parametric, unsupervised technique for GEBD. Our approach entails two algorithms utilizing optical flow: (i) Pixel Tracking and (ii) Flow Normalization. By conducting thorough experimentation on the challenging Kinetics-GEBD and TAPOS datasets, our results establish FlowGEBD as the new state-of-the-art (SOTA) among unsupervised methods. FlowGEBD exceeds the neural models on the Kinetics-GEBD dataset by obtaining an F1@0.05 score of 0.713 with an absolute gain of 31.7% compared to the unsupervised baseline and achieves an average F1 score of 0.623 on the TAPOS validation dataset.

关键词： Algorithms Applications Smartphones / end user devices Video recognition and understanding

来源：评论

学校读者我要写书评

暂无评论

Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation

Coarse-to-Fine Q-attention: Efficient Learning for Visual Ro...

引用

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

作者： James, Stephen Wada, Kentaro Laidlow, Tristan Davison, Andrew J. Imperial Coll London Dyson Robot Lab London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what part of the scene to `zoom' into. When this `zooming' behaviour is applied iteratively, it results in a near-lossless discretisation of the translation space, and allows the use of a discrete action, deep Q-learning method. We show that our new coarseto-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations.

关键词： Visualization computer vision Q-learning Machine vision Robot vision systems pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

ShadowSense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection from RGB-Thermal Drone Imagery

ShadowSense: Unsupervised Domain Adaptation and Feature Fusi...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kapil, Rudraksh Marvasti-Zadeh, Seyed Mojtaba Erbilgin, Nadir Ray, Nilanjan Univ Alberta Edmonton AB Canada

ISBN: (纸本)9798350318920;9798350318937

Accurate detection of individual tree crowns from remote sensing data poses a significant challenge due to the dense nature of forest canopy and the presence of diverse environmental variations, e.g., overlapping canopies, occlusions, and varying lighting conditions. Additionally, the lack of data for training robust models adds another limitation in effectively studying complex forest conditions. This paper presents a novel method for detecting shadowed tree crowns and provides a challenging dataset comprising roughly 50k paired RGB-thermal images to facilitate future research for illumination-invariant detection. The proposed method (ShadowSense) is entirely self-supervised, leveraging domain adversarial training without source domain annotations for feature extraction and foreground feature alignment for feature pyramid networks to adapt domain-invariant representations by focusing on visible foreground regions, respectively. It then fuses complementary information of both modalities to effectively improve upon the predictions of an RGB-trained detector and boost the overall accuracy. Extensive experiments demonstrate the superiority of the proposed method over both the baseline RGB-trained detector and state-of-the-art techniques that rely on unsupervised domain adaptation or early image fusion. Our code and data are available: https://***/rudrakshkapil/ShadowSense.

关键词： Algorithms Applications Image recognition and understanding Remote Sensing

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 << < 168 169 170 171 172 173 174 175 176 177 > >>

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：