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

20,858 篇 会议
105 篇 期刊文献
43 册 图书

馆藏范围

21,005 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,618 篇 工学
- 11,054 篇 计算机科学与技术...
- 2,651 篇 机械工程
- 2,251 篇 软件工程
- 914 篇 光学工程
- 885 篇 电气工程
- 528 篇 控制科学与工程
- 476 篇 信息与通信工程
- 216 篇 测绘科学与技术
- 135 篇 生物工程
- 127 篇 生物医学工程（可授...
- 98 篇 电子科学与技术（可...
- 92 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 40 篇 建筑学
- 40 篇 化学工程与技术
- 39 篇 土木工程
- 37 篇 交通运输工程
- 35 篇 力学（可授工学、理...
- 33 篇 航空宇航科学与技...
3,494 篇 医学
- 3,489 篇 临床医学
- 32 篇 基础医学(可授医学...
2,246 篇 理学
- 1,144 篇 物理学
- 1,081 篇 数学
- 401 篇 生物学
- 384 篇 统计学（可授理学、...
- 245 篇 系统科学
- 46 篇 化学
343 篇 管理学
- 176 篇 管理科学与工程(可...
- 168 篇 图书情报与档案管...
- 34 篇 工商管理
31 篇 法学
19 篇 农学
15 篇 教育学
8 篇 经济学
5 篇 艺术学
2 篇 军事学
1 篇 文学

主题

8,141 篇 computer vision
2,886 篇 training
2,839 篇 pattern recognit...
1,809 篇 computational mo...
1,715 篇 visualization
1,492 篇 cameras
1,433 篇 three-dimensiona...
1,433 篇 feature extracti...
1,366 篇 shape
1,360 篇 face recognition
1,242 篇 image segmentati...
1,135 篇 robustness
1,124 篇 semantics
992 篇 computer archite...
984 篇 object detection
982 篇 layout
959 篇 benchmark testin...
935 篇 codes
899 篇 computer science
898 篇 object recogniti...

机构

174 篇 univ sci & techn...
158 篇 univ chinese aca...
153 篇 carnegie mellon ...
145 篇 chinese univ hon...
109 篇 microsoft resear...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
95 篇 tsinghua univers...
91 篇 microsoft res as...
90 篇 tsinghua univ pe...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
77 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
72 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
65 篇 google res mount...

作者

80 篇 van gool luc
70 篇 zhang lei
58 篇 timofte radu
48 篇 yang yi
47 篇 luc van gool
46 篇 xiaoou tang
44 篇 tian qi
43 篇 darrell trevor
42 篇 loy chen change
42 篇 sun jian
41 篇 qi tian
40 篇 li stan z.
38 篇 li fei-fei
37 篇 chen xilin
36 篇 shan shiguang
35 篇 zhou jie
35 篇 vasconcelos nuno
35 篇 liu yang
35 篇 torralba antonio
34 篇 liu xiaoming

语言

20,982 篇 英文
10 篇 中文
5 篇 土耳其文
5 篇 其他
2 篇 日文
2 篇 葡萄牙文

检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21006 条记录，以下是4851-4860 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：

SpatialVLM: Endowing vision-Language Models with Spatial Reasoning Capabilities

SpatialVLM: Endowing Vision-Language Models with Spatial Rea...

引用

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

作者： Chen, Boyuan Xu, Zhuo Kirman, Sean Ichter, Brian Sadigh, Dorsa Guibas, Leonidas Xia, Fei Google DeepMind London England Google Res Mountain View CA USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350353006

Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While vision Language Models (VLM) have demonstrated remarkable performance in certain VQA benchmarks, they still lack capabilities in 3D spatial reasoning, such as recognizing quantitative relationships of physical objects like distances or size difference. We hypothesize that VLMs' limited spatial reasoning capability is due to the lack of 3D spatial knowledge in training data and aim to solve this problem by training VLMs with Internet-scale spatial reasoning data. To this end, we present a system to facilitate this approach. We first develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. We then investigate various factors in training recipe including data quality, training pipeline and VLM architecture. Our work features the first Internet-scale 3D spatial reasoning dataset in metric space. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA. Finally, we demonstrate that this VLM unlocks novel downstream applications in chain-of-thought spatial reasoning and robotics due to its quantitative estimation capability. Website: https://***/

关键词： large language model multimodal spatial reasoning vision language model

来源：评论

学校读者我要写书评

暂无评论

Search-Map-Search: A Frame Selection Paradigm for Action recognition

Search-Map-Search: A Frame Selection Paradigm for Action Rec...

引用

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

作者： Zhao, Mingjun Yu, Yakun Wang, Xiaoli Yang, Lei Niu, Di Univ Alberta Edmonton AB Canada Tencent Shenzhen Peoples R China Tencent Edmonton AB Canada

ISBN: (纸本)9798350301298

Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. Frame selection aims to extract the most informative and representative frames to help a model better understand video content. Existing frame selection methods either individually sample frames based on per-frame importance prediction, without considering interaction among frames, or adopt reinforcement learning agents to find representative frames in succession, which are costly to train and may lead to potential stability issues. To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity. By combining search with learning, the proposed method can better capture frame interactions while incurring a low inference overhead. Specifically, we first propose a hierarchical search method conducted on each training video to search for the optimal combination of frames with the lowest error on the downstream task. A feature mapping function is then learned to map the frames of a video to the representation of its target optimal frame combination. During inference, another search is performed on an unseen video to select a combination of frames whose feature representation is close to the projected feature representation. Extensive experiments based on several action recognition benchmarks demonstrate that our frame selection method effectively improves performance of action recognition models, and significantly outperforms a number of competitive baselines.

关键词： Video: Action and event understanding

来源：评论

学校读者我要写书评

暂无评论

Adaptive metric nearest neighbor classification

Adaptive metric nearest neighbor classification

引用

ieee conference on computer vision and pattern recognition (cvpr 2000)

作者： Domeniconi, C Peng, J Gunopulos, D Univ Calif Riverside Dept Comp Sci Riverside CA 92521 USA

ISBN: (纸本)0769506623

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a local adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of simulated and real world data.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Improving Transferability of Adversarial Examples with Input Diversity 32

Improving Transferability of Adversarial Examples with Input...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xie, Cihang Zhang, Zhishuai Zhou, Yuyin Bai, Song Wang, Jianyu Ren, Zhou Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Univ Oxford Oxford England Baidu Res Beijing Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9781728132938

Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples - crafted by adding human-imperceptible perturbations to clean images. However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black-box setting, where the attackers have no knowledge of the model structure and parameters. To this end, we propose to improve the transferability of adversarial examples by creating diverse input patterns. Instead of only using the original images to generate adversarial examples, our method applies random transformations to the input images at each iteration. Extensive experiments on ImageNet show that the proposed attack method can generate adversarial examples that transfer much better to different networks than existing baselines. By evaluating our method against top defense solutions and official baselines from NIPS 2017 adversarial competition, the enhanced attack reaches an average success rate of 73.0%, which outperforms the top-1 attack submission in the NIPS competition by a large margin of 6.6%. We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future.

关键词： Categorization Deep Learning recognition: Detection Retrieval Statistical Learning

来源：评论

学校读者我要写书评

暂无评论

User-Guided Variable Rate Learned Image Compression

User-Guided Variable Rate Learned Image Compression

引用

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

作者： Gupta, Rushil Suryateja, B., V Kapoor, Nikhil Jaiswal, Rajat Nangi, Sharmila Kulkarni, Kuldeep Adobe Res Bengaluru India Indian Inst Technol Delhi Delhi India Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a learning-based image compression method that achieves any arbitrary input bitrate via user-guided bit allocation to preferred regions. We verify our hypothesis of incorporating user guidance for bitrate control by experimenting with alternatives that do not have any guidance. We conduct extensive evaluation on CelebA-HQ and CityScapes dataset using standard quantitative metrics and human studies showing that our single model for multiple bitrates achieves similar or better performance as compared to previous learned image compression methods that require re-training for each new bitrate.

关键词： Measurement computer vision Image coding conferences Computational modeling Bit rate pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval

Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Lang...

引用

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

作者： Liu, Yang Chen, Qingchao Albanie, Samuel Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Natl Inst Hlth Data Sci Beijing Peoples R China Univ Oxford Visual Geometry Grp Oxford England Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9781665445092

In this paper, we study the task of visual-text retrieval in the highly practical setting in which labelled visual data with paired text descriptions are available in one domain (the "source"), but only unlabelled visual data (without text descriptions) are available in the domain of interest (the "target"). We propose the ADAPTIVE CROSS-MODAL PROTOTYPES framework which seeks to enable target domain retrieval by learning cross-modal visual-text representations while minimising both uni-modal and cross-modal distribution shift across the source and target domains. Our approach is built upon two key ideas: first, we encode the inductive bias that the learned cross-modal representations should be compositional with respect to concepts in each modality-this is achieved through clustering pretrained uni-modal features across each domain and designing a careful regularisation scheme to preserve the resulting structure. Second, we employ mutual information maximisation between cross-modal representations in the source and target domains during learning-this provides a mechanism that preserves commonalities between the domains while discarding signal in each that cannot be inferred from the other. We showcase our approach for the task of cross-domain visual-text retrieval, outperforming existing approaches for both images and videos.

关键词： Visualization computer vision Prototypes pattern recognition Task analysis Mutual information Videos

来源：评论

学校读者我要写书评

暂无评论

StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Di...

引用

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

作者： Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON out-performs the baselines in qualitative and quantitative eval-uation, showing promising quality in arbitrary person images. Our code is available at https://***/ rlawjdghek/StableVITON.

关键词： computer vision Diffusion Model Virtual Try-On

来源：评论

学校读者我要写书评

暂无评论

Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification

Refining Pseudo Labels with Clustering Consensus over Genera...

引用

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

作者： Zhang, Xiao Ge, Yixiao Qiao, Yu Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Xidian Univ Sch CST Xian Peoples R China

ISBN: (纸本)9781665445092

Unsupervised object re-identification targets at learning discriminative representations for object retrieval without any annotations. Clustering-based methods [27, 46, 10] conduct training with the generated pseudo labels and currently dominate this research direction. However;they still suffer from the issue of pseudo label noise. To tackle the challenge, we propose to properly estimate pseudo label similarities between consecutive training generations with clustering consensus and refine pseudo labels with temporally propagated and ensembled pseudo labels. To the best of our knowledge, this is the first attempt to leverage the spirit of temporal ensembling [25] to improve classification with dynamically changing classes over generations. The proposed pseudo label refinery strategy is simple yet effective and can be seamlessly integrated into existing clustering-based unsupervised re-identification methods. With our proposed approach, state-of-the-art method [16] can be further boosted with up to 8.8% mAP improvements on the challenging MSMT17 [39] dataset.

关键词： Training computer vision Annotations Refining pattern recognition Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Positive Sample Propagation along the Audio-Visual Event Line

Positive Sample Propagation along the Audio-Visual Event Lin...

引用

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

作者： Zhou, Jinxing Zheng, Liang Zhong, Yiran Hao, Shijie Wang, Meng Hefei Univ Technol Hefei Anhui Peoples R China Intelligent Interconnected Syst Lab Anhui Prov Hefei Anhui Peoples R China Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. In order to learn discriminative features for a classifier, it is pivotal to identify the helpful (or positive) audio-visual segment pairs while filtering out the irrelevant ones, regardless whether they are synchronized or not. To this end, we propose a new positive sample propagation (PSP) module to discover and exploit the closely related audio-visual pairs by evaluating the relationship within every possible pair. It can be done by constructing an all-pair similarity map between each audio and visual segment, and only aggregating the features from the pairs with high similarity scores. To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed. We also propose a new weighting branch to better exploit the temporal correlations in weakly supervised setting. We perform extensive experiments on the public AVE dataset and achieve new state-of-the-art accuracy in both fully and weakly supervised settings, thus verifying the effectiveness of our method.

关键词： Location awareness Visualization computer vision Correlation Filtering Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision 30

Link the head to the "beak": Zero Shot Learning from Noisy T...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Elhoseiny, Mohamed Zhu, Yizhe Zhang, Han Elgammal, Ahmed Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA Facebook AI Res New Brunswick NJ USA

ISBN: (纸本)9781538604571

In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. For instance, this learning process enables terms like "beak" to be sparsely linked to the visual representation of parts like head, while reduces the effect of non-visual terms like "migrate" on classifier prediction. Images are encoded by a part-based CNN that detect bird parts and learn part-specific representation. Part-based visual classifiers are predicted from text descriptions of unseen visual classifiers to facilitate classification without training images (also known as zero-shot recognition). We performed our experiments on CUBirds 2011 dataset and improves the state-of-the-art text-based zero-shot recognition results from 34.7% to 43.6%. We also created large scale benchmarks on North American Bird Images augmented with text descriptions, where we also show that our approach outperforms existing methods. Our code, data, and models are publically available link [1].

关键词： Visualization Birds Training Head Noise measurement Image recognition Head Image recognition Noise measurement Visualization texts Avifauna Functional training Beak Classifiers Training visual representation

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 << < 482 483 484 485 486 487 488 489 490 491 > >>

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：