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

23,134 篇 会议
91 篇 期刊文献
15 册 图书

馆藏范围

23,239 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,630 篇 工学
- 11,162 篇 计算机科学与技术...
- 3,337 篇 软件工程
- 2,414 篇 机械工程
- 1,662 篇 光学工程
- 1,204 篇 电气工程
- 972 篇 控制科学与工程
- 738 篇 信息与通信工程
- 381 篇 仪器科学与技术
- 322 篇 生物工程
- 239 篇 生物医学工程（可授...
- 188 篇 电子科学与技术（可...
- 109 篇 化学工程与技术
- 104 篇 安全科学与工程
- 99 篇 测绘科学与技术
- 85 篇 建筑学
- 82 篇 土木工程
- 82 篇 交通运输工程
- 56 篇 力学（可授工学、理...
3,696 篇 医学
- 3,684 篇 临床医学
- 76 篇 基础医学(可授医学...
3,137 篇 理学
- 1,880 篇 物理学
- 1,604 篇 数学
- 547 篇 统计学（可授理学、...
- 466 篇 生物学
- 243 篇 系统科学
- 107 篇 化学
491 篇 管理学
- 290 篇 图书情报与档案管...
- 212 篇 管理科学与工程(可...
- 74 篇 工商管理
252 篇 艺术学
- 251 篇 设计学（可授艺术学...
58 篇 法学
38 篇 农学
25 篇 教育学
19 篇 经济学
10 篇 军事学
3 篇 文学

主题

10,395 篇 computer vision
3,893 篇 pattern recognit...
3,101 篇 training
2,104 篇 computational mo...
1,897 篇 visualization
1,799 篇 cameras
1,487 篇 feature extracti...
1,475 篇 three-dimensiona...
1,464 篇 shape
1,447 篇 image segmentati...
1,287 篇 robustness
1,234 篇 computer archite...
1,213 篇 semantics
1,112 篇 benchmark testin...
1,111 篇 conferences
1,104 篇 layout
1,093 篇 object detection
1,085 篇 computer science
1,026 篇 codes
907 篇 face recognition

机构

137 篇 univ sci & techn...
124 篇 univ chinese aca...
121 篇 chinese univ hon...
108 篇 tsinghua univers...
108 篇 carnegie mellon ...
105 篇 microsoft resear...
97 篇 zhejiang univ pe...
91 篇 swiss fed inst t...
85 篇 university of sc...
84 篇 zhejiang univers...
81 篇 shanghai ai lab ...
79 篇 university of ch...
75 篇 shanghai jiao to...
69 篇 microsoft res as...
68 篇 alibaba grp peop...
66 篇 adobe research
65 篇 national laborat...
64 篇 peking univ peop...
61 篇 univ oxford oxfo...
59 篇 peng cheng labor...

作者

80 篇 van gool luc
71 篇 timofte radu
65 篇 zhang lei
43 篇 luc van gool
40 篇 yang yi
37 篇 loy chen change
34 篇 li stan z.
33 篇 liu yang
33 篇 xiaoou tang
33 篇 murino vittorio
33 篇 chen chen
33 篇 qi tian
33 篇 li fei-fei
32 篇 tian qi
32 篇 sun jian
30 篇 ying shan
30 篇 pascal fua
29 篇 darrell trevor
28 篇 li xin
28 篇 hanqing lu

语言

23,161 篇 英文
52 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

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

共 23240 条记录，以下是4951-4960 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：

Omni Aggregation Networks for Lightweight Image Super-Resolution

Omni Aggregation Networks for Lightweight Image Super-Resolu...

引用

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

作者： Wang, Hang Chen, Xuanhong Ni, Bingbing Liu, Yutian Liu, Jinfan Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions. To tackle these drawbacks, this work proposes two enhanced components under a new Omni-SR architecture. First, an Omni Self-Attention (OSA) block is proposed based on dense interaction principle, which can simultaneously model pixel-interaction from both spatial and channel dimensions, mining the potential correlations across omni-axis (i.e., spatial and channel). Coupling with mainstream window partitioning strategies, OSA can achieve superior performance with compelling computational budgets. Second, a multi-scale interaction scheme is proposed to mitigate sub-optimal ERF (i.e., premature saturation) in shallow models, which facilitates local propagation and meso-/global-scale interactions, rendering an omni-scale aggregation building block. Extensive experiments demonstrate that Omni-SR achieves record-high performance on lightweight super-resolution benchmarks (e.g., 26.95dB@Urban100 x4 with only 792K parameters). Our code is available at https://***/Francis0625/Omni-SR.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer

FlatFormer: Flattened Window Attention for Efficient Point C...

引用

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

作者： Liu, Zhijian Yang, Xinyu Tang, Haotian Yang, Shang Han, Song MIT Cambridge MA 02139 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Transformer, as an alternative to CNN, has been proven effective in many modalities (e.g., texts and images). For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of-the-art level. However, their latency lags behind sparse convolution-based models (3x slower), hindering their usage in resource-constrained, latency-sensitive applications (such as autonomous driving). This inefficiency comes from point clouds' sparse and irregular nature, whereas transformers are designed for dense, regular workloads. This paper presents FlatFormer to close this latency gap by trading spatial proximity for better computational regularity. We first flatten the point cloud with window-based sorting and partition points into groups of equal sizes rather than windows of equal shapes. This effectively avoids expensive structuring and padding overheads. We then apply self-attention within groups to extract local features, alternate sorting axis to gather features from different directions, and shift windows to exchange features across groups. FlatFormer delivers state-of-the-art accuracy on Waymo Open Dataset with 4.6x speedup over (transformer-based) SST and 1.4x speedup over (sparse convolutional) CenterPoint. This is the first point cloud transformer that achieves real-time performance on edge GPUs and is faster than sparse convolutional methods while achieving on-par or even superior accuracy on large-scale benchmarks.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics

Improving Semi-Supervised Domain Adaptation Using Effective ...

引用

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

作者： Singh, Anurag Doraiswamy, Naren Takamuku, Sawa Bhalerao, Megh Dutta, Titir Biswas, Soma Chepuri, Aditya Vengatesan, Balasubramanian Natori, Naotake Indian Inst Sci Bangalore Karnataka India Aisin Corp Toyota Japan Aisin Automot Haryana Pvt Ltd Bangalore Karnataka India

ISBN: (纸本)9781665448994

Recently, semi-supervised domain adaptation (SSDA) approaches have shown impressive performance for the domain adaptation task. They effectively utilize few labeled target samples along with the unlabeled data to account for the distribution shift across the source and target domains. In this work, we make three-fold contributions, concentrating on the role of target samples and semantics for the SSDA task. First, we observe that choosing a few, but an equal number of labeled samples from each class in the target domain requires a significant amount of manual effort. To address this, we propose an active learning-based framework by modeling both the sample diversity and the classifier uncertainty. By utilizing k-means initialized cluster centers for picking a small pool of diverse unlabeled target samples, we compute a novel classifier adaptation uncertainty term to select the most effective samples from this pool, which are queried to obtain their true labels from an oracle. Second, we propose to weigh the hard target samples more, without explicitly using their predicted, possibly incorrect labels, which guides the adaptation process. Third, we note that irrespective of the domain shift, the semantics of the classes remain unchanged, so they can be effectively utilized for this task. We show that initializing the class-representations or prototypes with the class-semantics helps in bridging the domain gap significantly. These along with adversarially learnt entropy objective results in a novel framework, termed STar (Select TARgets), which sets a new state-of-the-art for the SSDA task.

关键词： computer vision Uncertainty conferences Computational modeling Semantics Prototypes Manuals

来源：评论

学校读者我要写书评

暂无评论

Motion-aware Contrastive Video Representation Learning via Foreground-background Merging

Motion-aware Contrastive Video Representation Learning via F...

引用

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

作者： Ding, Shuangrui Li, Maomao Yang, Tianyu Qian, Rui Xu, Haohang Chen, Qingyi Wang, Jue Xiong, Hongkai Shanghai Jiao Tong Univ Shanghai Peoples R China Tencent AI Lab Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In light of the success of contrastive learning in the image domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate video representation learning. When naively pulling two augmented views of a video closer, the model however tends to learn the common static background as a shortcut but fails to capture the motion information, a phenomenon dubbed as background bias. Such bias makes the model suffer from weak generalization ability, leading to worse performance on downstream tasks such as action recognition. To alleviate such bias, we propose Foreground-background Merging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others. Specifically, without any off-the-shelf detector, we extract the moving foreground out of background regions via the frame difference and color statistics, and shuffle the background regions among the videos. By leveraging the semantic consistency between the original clips and the fused ones, the model focuses more on the motion patterns and is debiased from the background shortcut. Extensive experiments demonstrate that FAME can effectively resist background cheating and thus achieve the state-of-the-art performance on downstream tasks across UCF101, HMDB51, and Diving48 datasets. The code and configurations are released at https://***/Mark12Ding/FAME.

关键词： Representation learning computer vision Image color analysis Merging Semantics Resists Detectors

来源：评论

学校读者我要写书评

暂无评论

Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data

Best of Both Worlds: Multimodal Contrastive Learning with Ta...

引用

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

作者： Hager, Paul Menten, Martin J. Rueckert, Daniel Tech Univ Munich Munich Germany Klinikum Rechts Der Isar Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pretrain multimodally and predict unimodally has risen. To address these needs, we propose the first self-supervised contrastive learning framework that takes advantage of images and tabular data to train unimodal encoders. Our solution combines SimCLR and SCARF, two leading contrastive learning strategies, and is simple and effective. In our experiments, we demonstrate the strength of our framework by predicting risks of myocardial infarction and coronary artery disease (CAD) using cardiac MR images and 120 clinical features from 40,000 UK Biobank subjects. Furthermore, we show the generalizability of our approach to natural images using the DVM car advertisement dataset. We take advantage of the high interpretability of tabular data and through attribution and ablation experiments find that morphometric tabular features, describing size and shape, have outsized importance during the contrastive learning process and improve the quality of the learned embeddings. Finally, we introduce a novel form of supervised contrastive learning, label as a feature (LaaF), by appending the ground truth label as a tabular feature during multimodal pretraining, outperforming all supervised contrastive baselines.

关键词： cell microscopy Medical and biological vision

来源：评论

学校读者我要写书评

暂无评论

P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior

P3Depth: Monocular Depth Estimation with a Piecewise Planari...

引用

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

作者： Patil, Vaishakh Sakaridis, Christos Liniger, Alexander Gool, Luc Van Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven PSI Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time. Based on knowledge about the high regularity of real 3D scenes, we propose a method that learns to selectively leverage information from coplanar pixels to improve the predicted depth. In particular, we introduce a piecewise planarity prior which states that for each pixel, there is a seed pixel which shares the same planar 3D surface with the former. Motivated by this prior, we design a network with two heads. The first head outputs pixel-level plane coefficients, while the second one outputs a dense off-set vector field that identifies the positions of seed pixels. The plane coefficients of seed pixels are then used to predict depth at each position. The resulting prediction is adaptively fused with the initial prediction from the first head via a learned confidence to account for potential deviations from precise local planarity. The entire architecture is trained end-to-end thanks to the differentiability of the proposed modules and it learns to predict regular depth maps, with sharp edges at occlusion boundaries. An extensive evaluation of our method shows that we set the new state of the art in supervised monocular depth estimation, surpassing prior methods on NYU Depth-v2 and on the Garg split of KITTI. Our method delivers depth maps that yield plausible 3D reconstructions of the input scenes.

关键词： Training computer vision Three-dimensional displays Codes Estimation computer architecture Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Percept...

引用

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

作者： Hong, Fangzhou Pan, Liang Cai, Zhongang Liu, Ziwei Nanyang Technol Univ S Lab Singapore Singapore SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-centric perception plays a vital role in vision and graphics. But their data annotations are prohibitively expensive. Therefore, it is desirable to have a versatile pretrain model that serves as a foundation for data-efficient downstream tasks transfer. To this end, we propose the Human-Centric Multi-Modal Contrastive Learning framework HCMoCo that leverages the multi-modal nature of human data (e.g. RGB, depth, 2D keypoints) for effective representation learning. The objective comes with two main challenges: dense pre-train for multi-modality data, efficient usage of sparse human priors. To tackle the challenges, we design the novel Dense Intra-sample Contrastive Learning and Sparse Structure-aware Contrastive Learning targets by hierarchically learning a modal-invariant latent space featured with continuous and ordinal feature distribution and structure-aware semantic consistency. HCMoCo provides pre-train for different modalities by combining heterogeneous datasets, which allows efficient usage of existing task-specific human data. Extensive experiments on four downstream tasks of different modalities demonstrate the effectiveness of HCMoCo, especially under data-efficient settings (7.16% and 12% improvement on DensePose Estimation and Human Parsing). Moreover, we demonstrate the versatility of HCMoCo by exploring cross-modality supervision and missing-modality inference, validating its strong ability in cross-modal association and reasoning. Codes are available at https://***/hongfz16/HCMoCo.

关键词： Representation learning Graphics computer vision Codes Annotations Semantics Estimation

来源：评论

学校读者我要写书评

暂无评论

Boosting Co-teaching with Compression Regularization for Label Noise

Boosting Co-teaching with Compression Regularization for Lab...

引用

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

作者： Chen, Yingyi Shen, Xi Hu, Shell Xu Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Leuven Belgium UPE Ecole Ponts LIGM UMR 8049 Champs Sur Marne France Upload AI LLC Houston TX USA

ISBN: (纸本)9781665448994

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching [5] to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M [28] and ANIMAL-10N [24]. On Clothing1M [28], our approach obtains 74.9% accuracy which is slightly better than that of DivideMix [12]. On ANIMAL-10N [24], we achieve 84.1% accuracy while the best public result by PLC [30] is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://***/yingyichen-cyy/Nested-Co-teaching.

关键词： Adaptation models computer vision Image coding conferences Neural networks Data compression Information retrieval

来源：评论

学校读者我要写书评

暂无评论

TopNet: Structural Point Cloud Decoder 32

TopNet: Structural Point Cloud Decoder

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tchapmi, Lyne P. Kosaraju, Vineet Rezatofighi, S. Hamid Reid, Ian Savarese, Silvio Stanford Univ Stanford CA 94305 USA Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781728132938

3D point cloud generation is of great use for 3D scene modeling and understanding. Real-world 3D object point clouds can be properly described by a collection of lowlevel and high-level structures such as surfaces, geometric primitives, semantic parts, etc. In fact, there exist many different representations of a 3D object point cloud as a set of point groups. Existing frameworks for point cloud generation either do not consider structure in their proposed solutions, or assume and enforce a specific structure/topology, e.g. a collection of manifolds or surfaces, for the generated point cloud of a 3D object. In this work, we propose a novel decoder that generates a structured point cloud without assuming any specific structure or topology on the underlying point set. Our decoder is softly constrained to generate a point cloud following a hierarchical rooted tree structure. We show that given enough capacity and allowing for redundancies, the proposed decoder is very flexible and able to learn any arbitrary grouping of points including any topology on the point set. We evaluate our decoder on the task of point cloud generation for 3D point cloud shape completion. Combined with encoders from existing frameworks, we show that our proposed decoder significantly outperforms state-of-the-art 3D point cloud completion methods on the Shapenet dataset.

关键词： 3D from Multiview and Sensors 3D from Single Image Deep Learning Physics-based vision and Shape-from-X RGBD sen

来源：评论

学校读者我要写书评

暂无评论

Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images

Interventional Bag Multi-Instance Learning On Whole-Slide Pa...

引用

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

作者： Lin, Tiancheng Yu, Zhimiao Hu, Hongyu Xu, Yi Chen, Chang Wen Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on improving the feature extractor and aggregator. However, one deficiency of these methods is that the bag contextual prior may trick the model into capturing spurious correlations between bags and labels. This deficiency is a confounder that limits the performance of existing MIL methods. In this paper, we propose a novel scheme, Interventional Bag Multi-Instance Learning (IBMIL), to achieve deconfounded bag-level prediction. Unlike traditional likelihood-based strategies, the proposed scheme is based on the backdoor adjustment to achieve the interventional training, thus is capable of suppressing the bias caused by the bag contextual prior. Note that the principle of IBMIL is orthogonal to existing bag MIL methods. Therefore, IBMIL is able to bring consistent performance boosting to existing schemes, achieving new state-of-the-art performance. Code is available at https://***/HHHedo/IBMIL.

关键词： cell microscopy Medical and biological vision

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：