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

19,438 篇 会议
46 篇 期刊文献
5 册 图书

馆藏范围

19,488 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

12,440 篇 工学
- 10,282 篇 计算机科学与技术...
- 2,395 篇 机械工程
- 2,007 篇 软件工程
- 813 篇 光学工程
- 531 篇 电气工程
- 419 篇 控制科学与工程
- 322 篇 信息与通信工程
- 210 篇 测绘科学与技术
- 80 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 70 篇 生物工程
- 60 篇 仪器科学与技术
- 38 篇 建筑学
- 36 篇 土木工程
- 33 篇 力学（可授工学、理...
- 31 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 20 篇 材料科学与工程（可...
- 20 篇 交通运输工程
3,409 篇 医学
- 3,408 篇 临床医学
1,980 篇 理学
- 1,006 篇 数学
- 973 篇 物理学
- 359 篇 统计学（可授理学、...
- 336 篇 生物学
- 231 篇 系统科学
- 24 篇 化学
258 篇 管理学
- 138 篇 管理科学与工程(可...
- 122 篇 图书情报与档案管...
- 27 篇 工商管理
19 篇 法学
- 19 篇 社会学
14 篇 农学
8 篇 教育学
7 篇 经济学
3 篇 军事学
3 篇 艺术学

主题

7,893 篇 computer vision
2,727 篇 training
2,680 篇 pattern recognit...
1,760 篇 computational mo...
1,644 篇 visualization
1,410 篇 cameras
1,372 篇 three-dimensiona...
1,327 篇 shape
1,213 篇 face recognition
1,207 篇 image segmentati...
1,164 篇 feature extracti...
1,109 篇 robustness
1,087 篇 semantics
983 篇 layout
959 篇 object detection
949 篇 computer archite...
942 篇 benchmark testin...
931 篇 codes
902 篇 computer science
859 篇 deep learning

机构

174 篇 univ sci & techn...
161 篇 carnegie mellon ...
148 篇 univ chinese aca...
144 篇 chinese univ hon...
110 篇 microsoft resear...
106 篇 tsinghua univ pe...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
92 篇 tsinghua univers...
89 篇 microsoft res as...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
76 篇 alibaba grp peop...
73 篇 university of sc...
73 篇 hong kong univ s...
72 篇 peking univ peop...
72 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
66 篇 shanghai jiao to...

作者

79 篇 van gool luc
70 篇 zhang lei
59 篇 timofte radu
48 篇 yang yi
47 篇 xiaoou tang
45 篇 luc van gool
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 qi tian
38 篇 li stan z.
36 篇 chen xilin
36 篇 torralba antonio
35 篇 vasconcelos nuno
35 篇 shan shiguang
35 篇 liu yang
34 篇 liu xiaoming
34 篇 tao dacheng

语言

19,484 篇 英文
2 篇 日文
2 篇 中文
1 篇 其他

检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000"

共 19489 条记录，以下是4771-4780 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：

Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis

Semantic-shape Adaptive Feature Modulation for Semantic Imag...

引用

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

作者： Lv, Zhengyao Li, Xiaoming Niu, Zhenxing Cao, Bing Zuo, Wangmeng Tomorrow Adv Life Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China Alibaba Grp Machine Intelligence Lab Hangzhou Peoples R China Tianjin Univ Tianjin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent years have witnessed substantial progress in semantic image synthesis, it is still challenging in synthesizing photo-realistic images with rich details. Most previous methods focus on exploiting the given semantic map, which just captures an object-level layout for an image. Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly inferred from an object's shape. In order to exploit the part-level layouts, we propose a Shape-aware Position Descriptor (SPD) to describe each pixel's positional feature, where object shape is explicitly encoded into the SPD feature. Furthermore, a Semantic-shape Adaptive Feature Modulation (SAFM) block is proposed to combine the given semantic map and our positional features to produce adaptively modulated features. Extensive experiments demonstrate that the proposed SPD and SAFM significantly improve the generation of objects with rich details. Moreover, our method performs favorably against the SOTA methods in terms of quantitative and qualitative evaluation. The source code and model are available at SAFM.

关键词： computer vision Adaptation models Codes Shape Image synthesis Convolution Semantics

来源：评论

学校读者我要写书评

暂无评论

BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

BatchFormer: Learning to Explore Sample Relationships for Ro...

引用

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

作者： Hou, Zhi Yu, Baosheng Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned issues, a variety of methods have been devised to explore the sample relationships in a vanilla way (i.e., from the perspectives of either the input or the loss function), failing to explore the internal structure of deep neural networks for learning with sample relationships. Inspired by this, we propose to enable deep neural networks themselves with the ability to learn the sample relationships from each mini-batch. Specifically, we introduce a batch transformer module or BatchFormer, which is then applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. By doing this, the proposed method enables the collaboration of different samples, e.g., the head-class samples can also contribute to the learning of the tail classes for long-tailed recognition. Furthermore, to mitigate the gap between training and testing, we share the classifier between with or without the BatchFormer during training, which can thus be removed during testing. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications without any bells and whistles, including the tasks of long-tailed recognition, compositional zero-shot learning, domain generalization, and contrastive learning. Code is made publicly available at https://***/zhihou7/BatchFormer.

关键词： Training Deep learning Representation learning computer vision Neural networks Tail Transformers

来源：评论

学校读者我要写书评

暂无评论

SyncMask: Synchronized Attentional Masking for Fashion-centric vision-Language Pretraining

SyncMask: Synchronized Attentional Masking for Fashion-centr...

引用

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

作者： Song, Chull Hwan Hwang, Taebaek Yoon, Jooyoung Choi, Shunghyun Gu, Yeong Hyeon Dealicious Inc Seoul South Korea Sejong Univ Seoul South Korea

ISBN: (纸本)9798350353006

vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets of-en exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to cases where some textual details are not visible in individual images. This mismatch, particularly when non-co-occurring elements are masked, undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling, thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem, we pro-pose Synchronized attentional Masking (SyncMask), which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model, ensuring a precise alignment between the two modalities. Additionally, we enhance grouped batch sampling with semi-hard negatives, effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach, outperforming existing methods in three downstream tasks.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way

Segment, Magnify and Reiterate: Detecting Camouflaged Object...

引用

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

作者： Jia, Qi Yao, Shuilian Liu, Yu Fan, Xin Liu, Risheng Luo, Zhongxuan Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

It is challenging to accurately detect camouflaged objects from their highly similar surroundings. Existing methods mainly leverage a single-stage detection fashion, while neglecting small objects with low-resolution fine edges requires more operations than the larger ones. To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion. Specifically, we design a new discriminative mask which makes the model attend on the fixation and edge regions. In addition, we leverage an attention-based sampler to magnify the object region progressively with no need of enlarging the image size. Extensive experiments show our SegMaR achieves remarkable and consistent improvements over other stateof-the-art methods. Especially, we surpass two competitive methods 7.4% and 20.0% respectively in average over standard evaluation metrics on small camouflaged objects. Additional studies provide more promising insights into SegMaR, including its effectiveness on the discriminative mask and its generalization to other network architectures. Code is available at https://***/dlut-dimt/SegMaR.

关键词： Measurement Image segmentation computer vision Codes Image edge detection Object detection Network architecture

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

Feature Transfer Learning for Face recognition with Under-Represented Data 32

Feature Transfer Learning for Face Recognition with Under-Re...

引用

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

作者： Yin, Xi Yu, Xiang Sohn, Kihyuk Liu, Xiaoming Chandraker, Manmohan Michigan State Univ E Lansing MI 48824 USA NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781728132938

Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects and the variance from regular ones are transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.

关键词： And Body Pose Biometrics Face Gesture

来源：评论

学校读者我要写书评

暂无评论

DETRs with Hybrid Matching

DETRs with Hybrid Matching

引用

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

作者： Jia, Ding Yuan, Yuhui He, Haodi Wu, Xiaopei Yu, Haojun Lin, Weihong Sun, Lei Zhang, Chao Hu, Han Peking Univ Beijing Peoples R China Stanford Univ Stanford CA USA Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. Code is available at: https://***/HDETR.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Event-based Synthetic Aperture Imaging with a Hybrid Network

Event-based Synthetic Aperture Imaging with a Hybrid Network

引用

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

作者： Zhang, Xiang Liao, Wei Yu, Lei Yang, Wen Xia, Gui-Song Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781665445092

Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on conventional frame-based cameras, leading to performance degeneration. To address these problems, we propose a novel SAI system based on the event camera which can produce asynchronous events with extremely low latency and high dynamic range. Thus, it can eliminate the interference of dense occlusions by measuring with almost continuous views, and simultaneously tackle the over/under exposure problems. To reconstruct the occluded targets, we propose a hybrid encoder-decoder network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs). In the hybrid network, the spatio-temporal information of the collected events is first encoded by SNN layers, and then transformed to the visual image of the occluded targets by a style-transfer CNN decoder. Through experiments, the proposed method shows remarkable performance in dealing with very dense occlusions and extreme lighting conditions, and high quality visual images can be reconstructed using pure event data.

关键词： Heuristic algorithms Lighting Dynamic range Apertures Cameras Visual effects pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Unified Language-driven Zero-shot Domain Adaptation

Unified Language-driven Zero-shot Domain Adaptation

引用

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

作者： Yang, Senqiao Tian, Zhuotao Jiang, Li Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9798350353006

This paper introduces Unified Language-driven Zero-shot Domain Adaptation ( ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task, particularly the requirement for domain IDs and domain-specific models, which may restrict flexibility and scalability. To overcome these issues, we propose a new framework for ULDA, consisting of Hierarchical Context Alignment (HCA), Domain Consistent Representation Learning (DCRL), and Text-Driven Rectifier (TDR). These components work synergistically to align simulated features with target text across multiple visual levels, retain semantic correlations between different regional representations, and rectify biases between simulated and real target visual features, respectively. Our extensive empirical evaluations demonstrate that this framework achieves competitive performance in both settings, surpassing even the model that requires domain-ID, showcasing its superiority and generalization ability. The proposed method is not only effective but also maintains practicality and efficiency, as it does not introduce additional computational costs during inference. The code is available on the project website(1).

关键词： Domain Adaptation Segmentation vision-Language Model

来源：评论

学校读者我要写书评

暂无评论

Deep Rectangling for Image Stitching: A Learning Baseline

Deep Rectangling for Image Stitching: A Learning Baseline

引用

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

作者： Nie, Lang Lin, Chunyu Liao, Kang Liu, Shuaicheng Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant irregular boundaries. To deal with this problem, existing image rectangling methods devote to searching an initial mesh and optimizing a target mesh to form the mesh deformation in two stages. Then rectangular images can be generated by warping stitched images. However, these solutions only work for images with rich linear structures, leading to noticeable distortions for portraits and landscapes with non-linear objects. In this paper, we address these issues by proposing the first deep learning solution to image rectangling. Concretely, we predefine a rigid target mesh and only estimate an initial mesh to form the mesh deformation, contributing to a compact one-stage solution. The initial mesh is predicted using a fully convolutional network with a residual progressive regression strategy. To obtain results with high content fidelity, a comprehensive objective function is proposed to simultaneously encourage the boundary rectangular, mesh shape-preserving, and content perceptually natural. Besides, we build the first image stitching rectangling dataset with a large diversity in irregular boundaries and scenes. Experiments demonstrate our superiority over traditional methods both quantitatively and qualitatively.

关键词： Deep learning computer vision Nonlinear distortion Search problems Linear programming pattern recognition Computational efficiency

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 << < 474 475 476 477 478 479 480 481 482 483 > >>

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：