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

11,745 篇 会议
8 篇 期刊文献

馆藏范围

11,753 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,136 篇 工学
- 7,671 篇 计算机科学与技术...
- 804 篇 机械工程
- 577 篇 软件工程
- 376 篇 电气工程
- 249 篇 控制科学与工程
- 208 篇 光学工程
- 85 篇 生物工程
- 83 篇 信息与通信工程
- 29 篇 生物医学工程（可授...
- 23 篇 电子科学与技术（可...
- 21 篇 化学工程与技术
- 15 篇 交通运输工程
- 14 篇 安全科学与工程
- 10 篇 网络空间安全
- 8 篇 仪器科学与技术
- 6 篇 材料科学与工程（可...
- 6 篇 动力工程及工程热...
3,191 篇 医学
- 3,187 篇 临床医学
- 11 篇 基础医学(可授医学...
- 7 篇 公共卫生与预防医...
478 篇 理学
- 213 篇 物理学
- 203 篇 系统科学
- 88 篇 生物学
- 52 篇 数学
- 29 篇 统计学（可授理学、...
- 21 篇 化学
55 篇 管理学
- 29 篇 图书情报与档案管...
- 28 篇 管理科学与工程(可...
- 12 篇 工商管理
17 篇 法学
- 15 篇 社会学
6 篇 农学
4 篇 教育学
2 篇 经济学
1 篇 军事学
1 篇 艺术学

主题

5,434 篇 computer vision
2,516 篇 training
2,087 篇 pattern recognit...
1,621 篇 computational mo...
1,435 篇 visualization
1,306 篇 three-dimensiona...
1,060 篇 semantics
981 篇 codes
968 篇 benchmark testin...
898 篇 computer archite...
884 篇 deep learning
762 篇 task analysis
681 篇 feature extracti...
536 篇 face recognition
527 篇 conferences
515 篇 transformers
515 篇 neural networks
479 篇 object detection
466 篇 image segmentati...
454 篇 cameras

机构

168 篇 univ sci & techn...
144 篇 univ chinese aca...
144 篇 tsinghua univ pe...
143 篇 carnegie mellon ...
135 篇 chinese univ hon...
112 篇 peng cheng lab p...
108 篇 zhejiang univ pe...
97 篇 swiss fed inst t...
92 篇 tsinghua univers...
92 篇 sensetime res pe...
88 篇 shanghai ai lab ...
85 篇 zhejiang univers...
84 篇 shanghai jiao to...
78 篇 peng cheng labor...
77 篇 university of sc...
77 篇 alibaba grp peop...
76 篇 univ hong kong p...
76 篇 tech univ munich...
76 篇 stanford univ st...
73 篇 university of ch...

作者

76 篇 timofte radu
64 篇 van gool luc
50 篇 zhang lei
44 篇 yang yi
40 篇 loy chen change
34 篇 tao dacheng
32 篇 liu yang
32 篇 chen chen
30 篇 zhou jie
30 篇 tian qi
30 篇 sun jian
28 篇 zha zheng-jun
27 篇 qi tian
26 篇 li xin
26 篇 vasconcelos nuno
26 篇 ying shan
25 篇 liu xiaoming
25 篇 luc van gool
25 篇 boxin shi
24 篇 zheng wei-shi

语言

11,746 篇 英文
7 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4961-4970 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：

OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer

引用

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

作者： Gupta, Akshita Narayan, Sanath Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Shah, Mubarak Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates IIT Hyderabad Hyderabad India Australian Natl Univ Canberra ACT Australia Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ CVL Linkoping Sweden Univ Cent Florida Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-theart for all settings on PASCAL VOC. Our code is available at https://***/akshitac8/OW-DETR.

关键词： Training computer vision Object detection Transformers pattern recognition Proposals Object recognition

来源：评论

学校读者我要写书评

暂无评论

SMPLicit: Topology-aware Generative Model for Clothed People

SMPLicit: Topology-aware Generative Model for Clothed People

引用

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

作者： Corona, Enric Pumarola, Albert Alenya, Guillem Pons-Moll, Gerard Moreno-Noguer, Francesc UPC CSIC Inst Robot & Informat Ind Barcelona Spain Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781665445092

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://***. edu/people/ecorona/smplicit/.

关键词： Geometry Training computer vision Three-dimensional displays Shape Computational modeling Clothing

来源：评论

学校读者我要写书评

暂无评论

Efficient Video Instance Segmentation via Tracklet Query and Proposal

Efficient Video Instance Segmentation via Tracklet Query and...

引用

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

作者： Wu, Jialian Yarram, Sudhir Liang, Hui Lan, Tian Yuan, Junsong Eledath, Jayan Medioni, Gerard SUNY Buffalo Buffalo NY 14260 USA Amazon Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These limitations are addressed by the recent VIS transformer (VisTR) [25] which performs VIS end-to-end within a clip. However, VisTR suffers from long training time due to its frame-wise dense attention. In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips. This paper proposes EfficientVIS, a Ally end-to-end framework with efficient training and inference. At the core are tracklet query and tracklet proposal that associate and segment regions-of-interest (RoIs) across space and time by an iterative query-video interaction. We further propose a correspondence learning that makes tracklets linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS requires 15x fewer training epochs while achieving state-of-the-art accuracy on the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance segmentation in a single end-to-end pass without data association at all.

关键词： Training computer vision Tracking Motion segmentation Streaming media Transformers Real-time systems

来源：评论

学校读者我要写书评

暂无评论

C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image

C-CAM: Causal CAM for Weakly Supervised Semantic Segmentatio...

引用

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

作者： Chen, Zhang Tian, Zhiqiang Zhu, Jihua Li, Ce Du, Shaoyi Xi An Jiao Tong Univ Xian Peoples R China Lanzhou Univ Technol Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, many excellent weakly supervised semantic segmentation (WSSS) works are proposed based on class activation mapping (CAM). However, there are few works that consider the characteristics of medical images. In this paper, we find that there are mainly two challenges of medical images in WSSS: i) the boundary of object foreground and background is not clear;ii) the co-occurrence phenomenon is very severe in training stage. We thus propose a Causal CAM (C-CAM) method to overcome the above challenges. Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain. The category-causality chain represents the image content (cause) affects the category (effect). The anatomy-causality chain represents the anatomical structure (cause) affects the organ segmentation (effect). Extensive experiments were conducted on three public medical image data sets. Our C-CAM generates the best pseudo masks with the DSC of 77.26%, 80.34% and 78.15% on ProMRI, ACDC and CHAOS compared with other CAM-like methods. The pseudo masks of C-CAM are further used to improve the segmentation performance for organ segmentation tasks. Our C-CAM achieves DSC of 83.83% on ProMRI and DSC of 87.54% on ACDC, which outperforms state-of-the-art WSSS methods. Our code is available at https://***/Tian-lab/C-CAM.

关键词： Training Chaos Image segmentation computer vision Codes Semantics Anatomical structure

来源：评论

学校读者我要写书评

暂无评论

HDR-NeRF: High Dynamic Range Neural Radiance Fields

HDR-NeRF: High Dynamic Range Neural Radiance Fields

引用

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

作者： Huang, Xin Zhang, Qi Feng, Ying Li, Hongdong Wang, Xuan Wang, Qing Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Tencent AI Lab Shenzhen Peoples R China Australian Natl Univ Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the simplified physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to +infinity), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range.

关键词： Photography Solid modeling computer vision Three-dimensional displays Image color analysis Computational modeling Transforms

来源：评论

学校读者我要写书评

暂无评论

ER3: A Unified Framework for Event Retrieval, recognition and Recounting 30

ER3: A Unified Framework for Event Retrieval, Recognition an...

引用

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

作者： Gao, Zhanning Hua, Gang Zhang, Dongqing Jojic, Nebojsa Wang, Le Xue, Jianru Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9781538604571

We develop a unified framework for complex event retrieval, recognition and recounting. The framework is based on a compact video representation that exploits the temporal correlations in image features. Our feature alignment procedure identifies and removes the feature redundancies across frames and outputs an intermediate tensor representation we call video imprint. The video imprint is then fed into a reasoning network, whose attention mechanism parallels that of memory networks used in language modeling. The reasoning network simultaneously recognizes the event category and locates the key pieces of evidence for event recounting. In event retrieval tasks, we show that the compact video representation aggregated from the video imprint achieves significantly better retrieval accuracy compared with existing methods. We also set new state of the art results in event recognition tasks with an additional benefit: The latent structure in our reasoning network highlights the areas of the video imprint and can be directly used for event recounting. As video imprint maps back to locations in the video frames, the network allows not only the identification of key frames but also specific areas inside each frame which are most influential to the decision process.

关键词： Feature extraction Cognition pattern recognition Neural networks Layout computer vision

来源：评论

学校读者我要写书评

暂无评论

StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions

StyleMesh: Style Transfer for Indoor 3D Scene Reconstruction...

引用

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

作者： Hoellein, Lukas Johnson, Justin Niessner, Matthias Tech Univ Munich Munich Germany Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We apply style transfer on mesh reconstructions of indoor scenes. This enables VR applications like experiencing 3D environments painted in the style of a favorite artist. Style transfer typically operates on 2D images, making stylization of a mesh challenging. When optimized over a variety of poses, stylization patterns become stretched out and inconsistent in size. On the other hand, model-based 3D style transfer methods exist that allow stylization from a sparse set of images, but they require a network at inference time. To this end, we optimize an explicit texture for the reconstructed mesh of a scene and stylize it jointly from all available input images. Our depth- and angle-aware optimization leverages surface normal and depth data of the underlying mesh to create a uniform and consistent stylization for the whole scene. Our experiments show that our method creates sharp and detailed results for the complete scene without view-dependent artifacts. Through extensive ablation studies, we show that the proposed 3D awareness enables style transfer to be applied to the 3D domain of a mesh. Our method I can be used to render a stylized mesh in real-time with traditional rendering pipelines.

关键词： Surface reconstruction Solid modeling computer vision Three-dimensional displays Pipelines Rendering (computer graphics) Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Dual Contrastive Learning for Unsupervised Image-to-Image Translation

Dual Contrastive Learning for Unsupervised Image-to-Image Tr...

引用

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

作者： Han, Junlin Shoeiby, Mehrdad Petersson, Lars Armin, Mohammad Ali DATA61 CSIRO Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665448994

Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data. Contrastive learning for Unpaired image-to-image Translation (CUT) yields state-of-the-art results in modeling unsupervised image-to-image translation by maximizing mutual information between input and output patches using only one encoder for both domains. In this paper, we propose a novel method based on contrastive learning and a dual learning setting (exploiting two encoders) to infer an efficient mapping between unpaired data. Additionally, while CUT suffers from mode collapse, a variant of our method efficiently addresses this issue. We further demonstrate the advantage of our approach through extensive ablation studies demonstrating superior performance comparing to recent approaches in multiple challenging image translation tasks. Lastly, we demonstrate that the gap between unsupervised methods and supervised methods can be efficiently closed.

关键词： computer vision conferences Training data pattern recognition Task analysis Mutual information

来源：评论

学校读者我要写书评

暂无评论

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation 31

DecideNet: Counting Varying Density Crowds Through Attention...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Jiang Gao, Chenqiang Meng, Deyu Hauptmann, Alexander G. Carnegie Mellon Univ Pittsburgh PA 15213 USA Chongqing Univ Posts & Telecommun Chongqing Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9781538664209

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.

关键词： Estimation Detectors Reliability Head computer vision Task analysis Visualization

来源：评论

学校读者我要写书评

暂无评论

CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection

CAT-Det: Contrastively Augmented Transformer for Multi-modal...

引用

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

作者： Zhang, Yanan Chen, Jiaxin Huang, Di Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection. However, it is quite difficult to sufficiently use them, due to large inter-modal discrepancies. To address this issue, we propose a novel framework, namely Contrastively Augmented Transformer for multi-modal 3D object Detection (CAT-Det). Specifically, CAT-Det adopts a two-stream structure consisting of a Point-former (PT) branch, an Imageformer (IT) branch along with a Cross-Modal Transformer (CMT) module. PT, IT and CMT jointly encode intra-modal and inter-modal long-range contexts for representing an object, thus fully exploring multi-modal information for detection. Furthermore, we propose an effective One-way Multi-modal Data Augmentation (OMDA) approach via hierarchical contrastive learning at both the point and object levels, significantly improving the accuracy only by augmenting point-clouds, which is free from complex generation of paired samples of the two modalities. Extensive experiments on the KIM benchmark show that CAT-Det achieves a new state-of-the-art, highlighting its effectiveness.

关键词： computer vision Three-dimensional displays Laser radar Navigation Object detection Benchmark testing Transformers

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：