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

12,844 篇 会议
13 篇 期刊文献
2 册 图书

馆藏范围

12,859 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,573 篇 工学
- 6,863 篇 计算机科学与技术...
- 880 篇 机械工程
- 814 篇 软件工程
- 435 篇 控制科学与工程
- 360 篇 光学工程
- 306 篇 电气工程
- 209 篇 仪器科学与技术
- 124 篇 信息与通信工程
- 91 篇 生物工程
- 62 篇 生物医学工程（可授...
- 39 篇 电子科学与技术（可...
- 34 篇 安全科学与工程
- 26 篇 化学工程与技术
- 21 篇 交通运输工程
- 20 篇 建筑学
- 18 篇 土木工程
2,957 篇 医学
- 2,956 篇 临床医学
- 15 篇 基础医学(可授医学...
- 12 篇 药学(可授医学、理...
700 篇 理学
- 359 篇 物理学
- 225 篇 数学
- 175 篇 系统科学
- 95 篇 统计学（可授理学、...
- 93 篇 生物学
- 22 篇 化学
201 篇 艺术学
- 201 篇 设计学（可授艺术学...
84 篇 管理学
- 59 篇 图书情报与档案管...
- 25 篇 管理科学与工程(可...
- 14 篇 工商管理
23 篇 法学
- 21 篇 社会学
5 篇 农学
4 篇 教育学
2 篇 经济学
1 篇 军事学

主题

6,464 篇 computer vision
2,688 篇 training
2,437 篇 pattern recognit...
1,780 篇 computational mo...
1,522 篇 visualization
1,348 篇 three-dimensiona...
1,091 篇 computer archite...
1,063 篇 semantics
997 篇 benchmark testin...
976 篇 codes
970 篇 conferences
854 篇 feature extracti...
830 篇 cameras
771 篇 task analysis
707 篇 deep learning
646 篇 image segmentati...
611 篇 object detection
595 篇 shape
554 篇 transformers
538 篇 neural networks

机构

132 篇 univ sci & techn...
122 篇 carnegie mellon ...
120 篇 tsinghua univ pe...
114 篇 univ chinese aca...
113 篇 chinese univ hon...
94 篇 tsinghua univers...
91 篇 zhejiang univ pe...
91 篇 swiss fed inst t...
85 篇 peng cheng lab p...
81 篇 university of ch...
80 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 peng cheng labor...
75 篇 university of sc...
69 篇 shanghai jiao to...
68 篇 shanghai jiao to...
67 篇 alibaba grp peop...
67 篇 stanford univ st...
66 篇 univ hong kong p...
64 篇 sensetime res pe...

作者

77 篇 timofte radu
63 篇 van gool luc
45 篇 zhang lei
36 篇 yang yi
36 篇 luc van gool
34 篇 tao dacheng
31 篇 loy chen change
29 篇 chen chen
28 篇 sun jian
28 篇 qi tian
25 篇 li xin
24 篇 liu yang
24 篇 tian qi
24 篇 ying shan
23 篇 wang xinchao
23 篇 zha zheng-jun
23 篇 boxin shi
21 篇 zhou jie
21 篇 vasconcelos nuno
20 篇 luo ping

语言

12,851 篇 英文
7 篇 其他
1 篇 中文

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

共 12859 条记录，以下是4931-4940 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：

360MonoDepth: High-Resolution 360° Monocular Depth Estimation

360MonoDepth: High-Resolution 360° Monocular Depth Estimati...

引用

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

作者： Rey-Area, Manuel Yuan, Mingze Richardt, Christian Univ Bath Bath Avon England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

360 degrees cameras can capture complete environments in a single shot, which makes 360 degrees imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360 degrees data, particularly for high resolutions like 2K (2048x 1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose a flexible framework for monocular depth estimation from high-resolution 360 degrees images using tangent images. We project the 360 degrees input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360 degrees depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://***/360monodepth/.

关键词： computer vision Image resolution Three-dimensional displays Codes Estimation Graphics processing units Virtual reality

来源：评论

学校读者我要写书评

暂无评论

CHEX: CHannel EXploration for CNN Model Compression

CHEX: CHannel EXploration for CNN Model Compression

引用

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

作者： Hou, Zejiang Qin, Minghai Sun, Fei Ma, Xiaolong Yuan, Kun Xu, Yi Chen, Yen-Kuang Jin, Rong Xie, Yuan Kung, Sun-Yuan Princeton Univ Princeton NJ 08544 USA Alibaba Grp Hangzhou Peoples R China Northeastern Univ Boston MA 02115 USA Dalian Univ Technol Dalian Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Channel pruning has been broadly recognized as an effective technique to reduce the computation and memory cost of deep convolutional neural networks. However, conventional pruning methods have limitations in that: they are restricted to pruning process only, and they require a fully pre-trained large model. Such limitations may lead to sub-optimal model quality as well as excessive memory and training cost. In this paper, we propose a novel Channel Exploration methodology, dubbed as CHEX, to rectify these problems. As opposed to pruning-only strategy, we propose to repeatedly prune and regrow the channels throughout the training process, which reduces the risk of pruning important channels prematurely. More exactly: From intra-layer's aspect, we tackle the channel pruning problem via a well-known column subset selection (CSS) formulation. From inter-layer's aspect, our regrowing stages open a path for dynamically re-allocating the number of channels across all the layers under a global channel sparsity constraint. In addition, all the exploration process is done in a single training from scratch without the need of a pre-trained large model. Experimental results demonstrate that CHEX can effectively reduce the FLOPs of diverse CNN architectures on a variety of computer vision tasks, including image classification, object detection, instance segmentation, and 3D vision. For example, our compressed ResNet-50 model on ImageNet dataset achieves 76% top-1 accuracy with only 25% FLOPs of the original ResNet-50 model, outperforming previous state-of-the-art channel pruning methods. The checkpoints and code are available at here.

关键词： Training computer vision Costs Image coding Three-dimensional displays Computational modeling pattern recognition

来源：评论

学校读者我要写书评

暂无评论

SimMatch: Semi-supervised Learning with Similarity Matching

SimMatch: Semi-supervised Learning with Similarity Matching

引用

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

作者： Zheng, Mingkai You, Shan Huang, Lang Wang, Fei Qian, Chen Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia SenseTime Res Hong Kong Peoples R China Univ Tokyo Tokyo Japan Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity relationship respected to other instances. Next, we instantiated a labeled memory buffer to fully leverage the ground truth labels on instance-level and bridge the gaps between the semantic and instance similarities. Finally, we proposed the unfolding and aggregation operation which allows these two similarities be isomorphically transformed with each other. In this way, the semantic and instance pseudo-labels can be mutually propagated to generate more high-quality and reliable matching targets. Extensive experimental results demonstrate that SimMatch improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Notably, with 400 epochs of training, SimMatch achieves 67.2%, and 74.4% Top-1 Accuracy with 1% and 10% labeled examples on ImageNet, which significantly outperforms the baseline methods and is better than previous semi-supervised learning frameworks.

关键词： Training computer vision Annotations Semantics Machine learning Semisupervised learning Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Recurrent Glimpse-based Decoder for Detection with Transformer

Recurrent Glimpse-based Decoder for Detection with Transform...

引用

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

作者： Chen, Zhe Zhang, Jing Tao, Dacheng Univ Sydney Sydney NSW Australia JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although detection with Transformer (DETR) is increasingly popular, its global attention modeling requires an extremely long training period to optimize and achieve promising detection performance. Alternative to existing studies that mainly develop advanced feature or embedding designs to tackle the training issue, we point out that the Region-of-Interest (RoI) based detection refinement can easily help mitigate the difficulty of training for DETR methods. Based on this, we introduce a novel REcurrent Glimpse-based decOder (REGO) in this paper. In particular, the REGO employs a multi-stage recurrent processing structure to help the attention of DETR gradually focus on foreground objects more accurately. In each processing stage, visual features are extracted as glimpse features from RoIs with enlarged bounding box areas of detection results from the previous stage. Then, a glimpse-based decoder is introduced to provide refined detection results based on both the glimpse features and the attention modeling outputs of the previous stage. In practice, REGO can be easily embedded in representative DETR variants while maintaining their fully end-to-end training and inference pipelines. In particular, REGO helps Deformable DETR achieve 44.8 AP on the MSCOCO dataset with only 36 training epochs, compared with the first DETR and the Deformable DETR that require 500 and 50 epochs to achieve comparable performance, respectively. Experiments also show that REGO consistently boosts the performance of different DETR detectors by up to 7% relative gain at the same setting of 50 training epochs. Code is available via https://***/zhechen/DeformableDETR-REGO.

关键词： Training Visualization computer vision Pipelines Detectors Feature extraction Transformers

来源：评论

学校读者我要写书评

暂无评论

DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

DCNAS: Densely Connected Neural Architecture Search for Sema...

引用

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

作者： Zhang, Xiong Xu, Hongmin Mo, Hong Tan, Jianchao Yang, Cheng Wang, Lei Ren, Wenqi Joyy Inc Singapore Singapore Kwai Inc AI Platform Bengaluru India Beihang Univ State Key Lab VR Beijing Peoples R China Chinese Acad Sci IIE SKLOIS Beijing Peoples R China

ISBN: (纸本)9781665445092

Existing NAS methods for dense image prediction tasks usually compromise on restricted search space or search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between realistic and proxy setting, we propose a novel Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset without proxy. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module and mixture layer to reduce the memory consumption of ample search space, hence favoring the proxyless searching. Compared with contemporary works, experiments reveal that the proxyless searching scheme is capable of bridging the gap between searching and training environments. Further, DCNAS achieves new state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and PASCAL-Context dataset.

关键词： Training Image segmentation Visualization computer vision Semantics Memory management Network architecture

来源：评论

学校读者我要写书评

暂无评论

Meta Agent Teaming Active Learning for Pose Estimation

Meta Agent Teaming Active Learning for Pose Estimation

引用

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

作者： Gong, Jia Fan, Zhipeng Ke, Qiuhong Rahmani, Hossein Liu, Jun Singapore Univ Technol & Design Singapore Singapore NYU New York NY 10003 USA Univ Melbourne Parkville Vic Australia Univ Lancaster Lancaster England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire. To reduce the human efforts on pose annotations, we propose a novel Meta Agent Teaming Active Learning (MATAL) framework to actively select and label informative images for effective learning. Our MATAL formulates the image selection procedure as a Markov Decision Process and learns an optimal sampling policy that directly maximizes the performance of the pose estimator based on the reward. Our framework consists of a novel state-action representation as well as a multi-agent team to enable batch sampling in the active learning procedure. The framework could be effectively optimized via Meta-Optimization to accelerate the adaptation to the gradually expanded labeled data during deployment. Finally, we show experimental results on both human hand and body pose estimation benchmark datasets and demonstrate that our method significantly outperforms all baselines continuously under the same amount of annotation budget. Moreover, to obtain similar pose estimation accuracy, our MATAL framework can save around 40% labeling efforts on average compared to state-of-the-art active learning frameworks.

关键词： computer vision Annotations Computational modeling Accelerated aging Pose estimation Markov processes Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual I...

引用

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

作者： Yu, Qifan Li, Juncheng Wei, Longhui Pang, Liang Ye, Wentao Qin, Bosheng Tang, Siliang Tian, Qi Zhuang, Yueting Zhejiang Univ Hangzhou Peoples R China Huawei Cloud Suzhou Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350353006

Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks. However, the hallucinations inherent in machine-generated data, which could lead to hallucinatory outputs in MLLMs, remain under-explored. This work aims to investigate various hallucinations (i.e., object, relation, attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors, we present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly, HalluciDoctor also indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations. Based on that, we execute counterfactual visual instruction expansion to balance data distribution, thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.(1)

关键词： Hallucinations Multi-modal Language Model vision-language reasoning

来源：评论

学校读者我要写书评

暂无评论

Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

Back-tracing Representative Points for Voting-based 3D Objec...

引用

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

作者： Cheng, Bowen Sheng, Lu Shi, Shaoshuai Yang, Ming Xu, Dong Beihang Univ Coll Software Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial votes from the surfaces of potential objects together with severe outlier votes from the cluttered backgrounds, which hampers full utilization of the information from the input point clouds. Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds. Therefore, this bottom-up and then top-down strategy in our BRNet enforces mutual consistency between the predicted vote centers and the raw surface points and thus achieves more reliable and flexible object localization and class prediction results. Our BRNet is simple but effective, which significantly outperforms the state-of-the-art methods on two large-scale point cloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in terms of mAP@0.50), while it is still lightweight and efficient.

关键词： Location awareness Visualization Three-dimensional displays Object detection Feature extraction pattern recognition Proposals

来源：评论

学校读者我要写书评

暂无评论

EKILA: Synthetic Media Provenance and Attribution for Generative Art

EKILA: Synthetic Media Provenance and Attribution for Genera...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Balan, Kar Agarwal, Shruti Jenni, Simon Parsons, Andy Gilbert, Andrew Collomosse, John University of Surrey United Kingdom Adobe Inc.

ISBN: (纸本)9798350302493

We present EKILA;a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance - determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation

Instance-aware Exploration-Verification-Exploitation for Ins...

引用

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

作者： Lei, Xiaohan Wang, Min Zhou, Wengang Li, Li Li, Houqiang Univ Sci & Technol China MoE Key Lab Brain Inspired Intelligent Percept & Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

As a new embodied vision task, Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment. The main challenge of this task lies in identifying the target object from different viewpoints while rejecting similar distractors. Existing ImageGoal Navigation methods usually adopt the simple Exploration-Exploitation framework and ignore the identification of specific instance during navigation. In this work, we propose to imitate the human behaviour of "getting closer to confirm" when distinguishing objects from a distance. Specifically, we design a new modular navigation framework named Instance-aware Exploration-Verification- Exploitation (IEVE) for instance-level image goal navigation. Our method allows for active switching among the exploration, verification, and exploitation actions, thereby facilitating the agent in making reasonable decisions under different situations. On the challenging HabitatMatterport 3D semantic (HM3D-SEM) dataset, our method surpasses previous state-of-the-art work, with a classical segmentation model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success). Our code will be made publicly available at https://***/XiaohanLei/IEVE.

关键词： Embodied vision Verification Visual Navigation

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 << < 490 491 492 493 494 495 496 497 498 499 > >>

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：