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

11,886 篇 会议
5 篇 期刊文献

馆藏范围

11,891 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,060 篇 工学
- 7,618 篇 计算机科学与技术...
- 796 篇 机械工程
- 688 篇 电气工程
- 361 篇 软件工程
- 228 篇 控制科学与工程
- 41 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...
- 7 篇 交通运输工程
- 6 篇 电子科学与技术（可...
- 6 篇 建筑学
- 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...
254 篇 理学
- 198 篇 系统科学
- 32 篇 物理学
- 21 篇 生物学
- 19 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...
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

11,850 篇 英文
40 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 11891 条记录，以下是1651-1660 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：

Efficient Feature Distillation for Zero-shot Annotation Object Detection

Efficient Feature Distillation for Zero-shot Annotation Obje...

引用

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

作者： Liu, Zhuoming Hu, Xuefeng Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9798350318920;9798350318937

We propose a new setting for detecting unseen objects called Zero-shot Annotation object Detection (ZAD). It expands the zero-shot object detection setting by allowing the novel objects to exist in the training images and restricts the additional information the detector uses to novel category names. Recently, to detect unseen objects, large-scale vision-language models (e.g., CLIP) are leveraged by different methods. The distillation-based methods have good overall performance but suffer from a long training schedule caused by two factors. First, existing work creates distillation regions biased to the base categories, which limits the distillation of novel category information. Second, directly using the raw feature from CLIP for distillation neglects the domain gap between the training data of CLIP and the detection datasets, which makes it difficult to learn the mapping from the image region to the vision-language feature space. To solve these problems, we propose Efficient feature distillation for Zero-shot Annotation object Detection (EZAD). Firstly, EZAD adapts the CLIP's feature space to the target detection domain by re-normalizing CLIP;Secondly, EZAD uses CLIP to generate distillation proposals with potential novel category names to avoid the distillation being overly biased toward the base categories. Finally, EZAD takes advantage of semantic meaning for regression to further improve the model performance. As a result, EZAD outperforms the previous distillation-based methods in COCO by 4% with a much shorter training schedule and achieves a 3% improvement on the LVIS dataset. Our code is available at https://***/dragonlzm/EZAD

关键词： Algorithms Algorithms Algorithms Image recognition and understanding Video recognition and understanding vision + language and/or other modalities

来源：评论

学校读者我要写书评

暂无评论

Grafting vision Transformers

Grafting Vision Transformers

引用

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

作者： Park, Jongwoo Kahatapitiya, Kumara Kim, Donghyun Sudalairaj, Shivchander Fan, Quanfu Ryoo, Michael S. SUNY Stony Brook Stony Brook NY 11794 USA MIT IBM Watson AI Lab Cambridge MA 02142 USA Amazon Seattle WA USA

ISBN: (纸本)9798350318920;9798350318937

vision Transformers (ViTs) have recently become the state-of-the-art across many computer vision tasks. In contrast to convolutional networks (CNNs), ViTs enable global information sharing even within shallow layers of a network, i.e., among high-resolution features. However, this perk was later overlooked with the success of pyramid architectures such as Swin Transformer, which show better performance-complexity trade-offs. In this paper, we present a simple and efficient add-on component (termed GrafT) that considers global dependencies and multi-scale information throughout the network, in both high- and low-resolution features alike. It has the flexibility of branching out at arbitrary depths and shares most of the parameters and computations of the backbone. GrafT shows consistent gains over various well-known models which includes both hybrid and pure Transformer types, both homogeneous and pyramid structures, and various self-attention methods. In particular, it largely benefits mobile-size models by providing high-level semantics. On the ImageNet-1k dataset, GrafT delivers +3.9%, +1.4%, and +1.9% top1 accuracy improvement to DeiT-T, Swin-T, and MobViT-XXS, respectively. Our code and models are at https: // github. com/ jongwoopark7978/ Graftingvision- Transformer.

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

Learning-based Spotlight Position Optimization for Non-Line-of-Sight Human Localization and Posture Classification

Learning-based Spotlight Position Optimization for Non-Line-...

引用

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

作者： Chandran, Sreenithy Yatagawa, Tatsuya Kubo, Hiroyuki Jayasuriya, Suren Arizona State Univ Tempe AZ 85287 USA Hitotsubashi Univ Kunitachi Tokyo Japan Chiba Univ Chiba Japan

ISBN: (纸本)9798350318920;9798350318937

Non-line-of-sight imaging (NLOS) is the process of estimating information about a scene that is hidden from the direct line of sight of the camera. NLOS imaging typically requires time-resolved detectors and a laser source for illumination, which are both expensive and computationally intensive to handle. In this paper, we propose an NLOS-based localization and posture classification technique that uses an off-the-shelf projector and camera system. We leverage a message-passing neural network to learn a visible scene geometry and predict the best position to be spotlighted by the projector that can maximize the NLOS signal. The neural network is trained end-to-end and the network parameters are optimized to maximize the NLOS performance. Unlike prior deep-learning-based NLOS techniques that assume planar relay walls, our system allows us to handle line-of-sight scenes where scene geometries are more arbitrary. Our method demonstrates state-of-the-art performance in object localization and position classification using both synthetic and real scenes.

关键词： Algorithms Algorithms Algorithms Computational photography image and video synthesis Low-level and physics-based vision Video recognition and understanding

来源：评论

学校读者我要写书评

暂无评论

RGBT-Dog: A Parametric Model and Pose Prior For Canine Body Analysis Data Creation

RGBT-Dog: A Parametric Model and Pose Prior For Canine Body ...

引用

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

作者： Deane, Jake Kearney, Sinead Kim, Kwang In Cosker, Darren Univ Bath Bath England POSTECH Pohang South Korea

ISBN: (纸本)9798350318920;9798350318937

While there exists a great deal of labeled in-the-wild human data, the same is not true for animals. Manually creating new labels for the full range of animal species would take years of effort from the community. We are also now seeing the emerging potential for computer vision methods in areas like animal conservation, which is an additional motivation for this direction of research. Key to our approach is the ability to easily generate as many labeled training images as we desire across a range of different modalities. To achieve this, we present a new large scale canine motion capture dataset and parametric canine body and texture model. These are used to produce the first large scale, multi-domain, multi-task dataset for canine body analysis comprising of detailed synthetic labels on both real images and fully synthetic images in a range of realistic poses. We also introduce the first pose prior for animals in the form of a variational pose prior for canines which is used to fit the parametric model to images of canines. We demonstrate the effectiveness of our labels for training computer vision models on tasks such as parts-based segmentation and pose estimation and show such models can generalise to other animal species without additional training.

关键词： Algorithms Algorithms Animals / Insects Applications 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

Deep orientation-aware functional maps: Tackling symmetry issues in Shape Matching

Deep orientation-aware functional maps: Tackling symmetry is...

引用

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

作者： Donati, Nicolas Corman, Etienne Ovsjanikov, Maks Ecole Polytech LIX Palaiseau France CNRS INRIA Paris France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

State-of-the-art fully intrinsic network for non-rigid shape matching are unable to disambiguate between shape inner symmetries. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on DiffusionNet, which makes our method robust to discretization changes, while adding a vector-field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors.

关键词： Deep learning computer vision Codes Shape computer architecture pattern matching

来源：评论

学校读者我要写书评

暂无评论

Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation

Rotation-Constrained Cross-View Feature Fusion for Multi-Vie...

引用

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

作者： Hisadome, Yoichiro Wu, Tianyi Qin, Jiawei Sugano, Yusuke Univ Tokyo Inst Ind Sci Komaba 4-6-1 Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Appearance-based gaze estimation has been actively studied in recent years. However, its generalization performance for unseen head poses is still a significant limitation for existing methods. This work proposes a generalizable multi-view gaze estimation task and a cross-view feature fusion method to address this issue. In addition to paired images, our method takes the relative rotation matrix between two cameras as additional input. The proposed network learns to extract rotatable feature representation by using relative rotation as a constraint and adaptively fuses the rotatable features via stacked fusion modules. This simple yet efficient approach significantly improves generalization performance under unseen head poses without significantly increasing computational cost. The model can be trained with random combinations of cameras without fixing the positioning and can generalize to unseen camera pairs during inference. Through experiments using multiple datasets, we demonstrate the advantage of the proposed method over baseline methods, including state-of-the-art domain generalization approaches. The code will be available at https://***/ut-vision/Rot-MVGaze.

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

Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation

Prompting classes: Exploring the Power of Prompt Class Learn...

引用

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

作者： Murugesan, Balamurali Hussain, Rukhshanda Bhattacharya, Rajarshi Ben Ayed, Ismail Dolz, Jose ETS Montreal Montreal PQ Canada Jadavpur Univ Kolkata India

ISBN: (纸本)9798350318920;9798350318937

Recently, CLIP-based approaches have exhibited remarkable performance on generalization and few-shot learning tasks, fueled by the power of contrastive language-vision pre-training. In particular, prompt tuning has emerged as an effective strategy to adapt the pre-trained language-vision models to downstream tasks by employing task-related textual tokens. Motivated by this progress, in this work we question whether other fundamental problems, such as weakly supervised semantic segmentation (WSSS), can benefit from prompt tuning. Our findings reveal two interesting observations that shed light on the impact of prompt tuning on WSSS. First, modifying only the class token of the text prompt results in a greater impact on the Class Activation Map (CAM), compared to arguably more complex strategies that optimize the context. And second, the class token associated with the image ground truth does not necessarily correspond to the category that yields the best CAM. Motivated by these observations, we introduce a novel approach based on a PrOmpt cLass lEarning (POLE) strategy. Through extensive experiments we demonstrate that our simple, yet efficient approach achieves SOTA performance in a well-known WSSS benchmark. These results highlight not only the benefits of language-vision models in WSSS but also the potential of prompt learning for this problem. The code is available at code link

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures vision + language and/or other modalities

来源：评论

学校读者我要写书评

暂无评论

Prompt Distribution Learning

Prompt Distribution Learning

引用

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

作者： Lu, Yuning Liu, Jianzhuang Zhang, Yonggang Liu, Yajing Tian, Xinmei Univ Sci & Technol China Hefei Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we provide high-quality task-related content for facilitating recognition. This prompt distribution learning is realized by an efficient approach that learns the output embeddings of prompts instead of the input embeddings. Thus, we can employ a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training. Extensive experiments on 12 datasets demonstrate that our method consistently and significantly outperforms existing methods. For example, with 1 sample per category, it relatively improves the average result by 9.1% compared to human-crafted prompts.

关键词： Training Visualization computer vision Adaptation models Gaussian distribution pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework

Use All The Labels: A Hierarchical Multi-Label Contrastive L...

引用

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

作者： Zhang, Shu Xu, Ran Xiong, Caiming Ramaiah, Chetan Salesforce Res Washington DC 20006 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint. The loss function is data driven and automatically adapts to arbitrary multi-label structures. Experiments on several datasets show that our relationship preserving embedding performs well on a variety of tasks and outperform the baseline supervised and self-supervised approaches. Code is available at https://***/salesforce/hierarchicalContrastiveLearning.

关键词： Representation learning computer vision Codes Speech recognition Task analysis Standards

来源：评论

学校读者我要写书评

暂无评论

Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval

Modality-Aware Representation Learning for Zero-shot Sketch-...

引用

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

作者： Lyou, Eunyi Lee, Doyeon Kim, Jooeun Lee, Joonseok Seoul Natl Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350318920;9798350318937

Zero-shot learning offers an efficient solution for a machine learning model to treat unseen categories, avoiding exhaustive data collection. Zero-shot Sketch-based Image Retrieval (ZS-SBIR) simulates real-world scenarios where it is hard and costly to collect paired sketch-photo samples. We propose a novel framework that indirectly aligns sketches and photos by contrasting them through texts, removing the necessity of access to sketch-photo pairs. With an explicit modality encoding learned from data, our approach disentangles modality-agnostic semantics from modality-specific information, bridging the modality gap and enabling effective cross-modal content retrieval within a joint latent space. From comprehensive experiments, we verify the efficacy of the proposed model on ZS-SBIR, and it can be also applied to generalized and fine-grained settings.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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 << < 162 163 164 165 166 167 168 169 170 171 > >>

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：