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

20,860 篇 会议
107 篇 期刊文献
43 册 图书

馆藏范围

21,009 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,622 篇 工学
- 11,058 篇 计算机科学与技术...
- 2,652 篇 机械工程
- 2,252 篇 软件工程
- 914 篇 光学工程
- 887 篇 电气工程
- 529 篇 控制科学与工程
- 477 篇 信息与通信工程
- 216 篇 测绘科学与技术
- 135 篇 生物工程
- 127 篇 生物医学工程（可授...
- 98 篇 电子科学与技术（可...
- 92 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 40 篇 建筑学
- 40 篇 化学工程与技术
- 39 篇 土木工程
- 37 篇 交通运输工程
- 35 篇 力学（可授工学、理...
- 33 篇 航空宇航科学与技...
3,494 篇 医学
- 3,489 篇 临床医学
- 32 篇 基础医学(可授医学...
2,247 篇 理学
- 1,145 篇 物理学
- 1,081 篇 数学
- 401 篇 生物学
- 384 篇 统计学（可授理学、...
- 245 篇 系统科学
- 46 篇 化学
343 篇 管理学
- 176 篇 管理科学与工程(可...
- 168 篇 图书情报与档案管...
- 34 篇 工商管理
31 篇 法学
19 篇 农学
15 篇 教育学
8 篇 经济学
5 篇 艺术学
2 篇 军事学
1 篇 文学

主题

8,143 篇 computer vision
2,886 篇 training
2,841 篇 pattern recognit...
1,809 篇 computational mo...
1,715 篇 visualization
1,493 篇 cameras
1,433 篇 three-dimensiona...
1,433 篇 feature extracti...
1,366 篇 shape
1,360 篇 face recognition
1,243 篇 image segmentati...
1,135 篇 robustness
1,124 篇 semantics
992 篇 computer archite...
985 篇 object detection
982 篇 layout
959 篇 benchmark testin...
935 篇 codes
900 篇 computer science
898 篇 object recogniti...

机构

174 篇 univ sci & techn...
158 篇 univ chinese aca...
153 篇 carnegie mellon ...
145 篇 chinese univ hon...
109 篇 microsoft resear...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
95 篇 tsinghua univers...
91 篇 microsoft res as...
90 篇 tsinghua univ pe...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
77 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
72 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
65 篇 google res mount...

作者

80 篇 van gool luc
70 篇 zhang lei
58 篇 timofte radu
48 篇 yang yi
47 篇 luc van gool
46 篇 xiaoou tang
44 篇 tian qi
43 篇 darrell trevor
42 篇 loy chen change
42 篇 sun jian
41 篇 qi tian
40 篇 li stan z.
38 篇 li fei-fei
37 篇 chen xilin
36 篇 shan shiguang
35 篇 zhou jie
35 篇 vasconcelos nuno
35 篇 liu yang
35 篇 torralba antonio
34 篇 liu xiaoming

语言

20,974 篇 英文
17 篇 其他
10 篇 中文
5 篇 土耳其文
2 篇 日文
2 篇 葡萄牙文

检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21010 条记录，以下是1631-1640 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：

Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion

Learning to Listen: Modeling Non-Deterministic Dyadic Facial...

引用

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

作者： Ng, Evonne Joo, Hanbyul Hu, Liwen Li, Hao Darrell, Trevor Kanazawa, Angjoo Ginosar, Shiry Univ Calif Berkeley Berkeley CA 94720 USA Seoul Natl Univ Seoul South Korea Pinscreen Los Angeles CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of corresponding listener motion. We combine the motion and speech audio of the speaker using a motion-audio cross attention transformer. Furthermore, we enable nondeterministic prediction by learning a discrete latent representation of realistic listener motion with a novel motion-encoding VQ-VAE. Our method organically captures the multimodal and non-deterministic nature of nonverbal dyadic interactions. Moreover, it produces realistic 3D listener facial motion synchronous with the speaker (see video). We demonstrate that our method outperforms baselines qualitatively and quantitatively via a rich suite of experiments. To facilitate this line of research, we introduce a novel and large in-the-wild dataset of dyadic conversations.

关键词： computer vision Three-dimensional displays Codes Computational modeling Oral communication Transformers Data models

来源：评论

学校读者我要写书评

暂无评论

One Step at a Time: Long-Horizon vision-and-Language Navigation with Milestones

One Step at a Time: Long-Horizon Vision-and-Language Navigat...

引用

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

作者： Song, Chan Hee Kil, Jihyung Pan, Tai-Yu Sadler, Brian M. Chao, Wei-Lun Su, Yu Ohio State Univ Columbus OH 43210 USA US Army Res Lab Adelphi MD USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task. Significant progress has been made in recent years, especially for tasks with short horizons. However, when it comes to long-horizon tasks with extended sequences of actions, an agent can easily ignore some instructions or get stuck in the middle of the long instructions and eventually fail the task. To address this challenge, we propose a model-agnostic milestone-based task tracker (M-TRACK) to guide the agent and monitor its progress. Specifically, we propose a milestone builder that tags the instructions with navigation and interaction milestones which the agent needs to complete step by step, and a milestone checker that systemically checks the agent's progress in its current milestone and determines when to proceed to the next. On the challenging ALFRED dataset, our M-TRACK leads to a notable 33% and 52% relative improvement in unseen success rate over two competitive base models.

关键词： computer vision Navigation Computational modeling Robot vision systems Machine learning Autonomous agents pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Multidimensional Belief Quantification for Label-Efficient Meta-Learning

Multidimensional Belief Quantification for Label-Efficient M...

引用

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

作者： Pandey, Deep Shankar Yu, Qi Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples introduces uncertainty, and quantifying model confidence for few-shot predictions is essential for many critical domains. Furthermore, few-shot tasks used in meta training are usually sampled randomly from a task distribution for an iterative model update, leading to high labeling costs and computational overhead in meta-training. We propose a novel uncertainty-aware task selection model for label efficient meta-learning. The proposed model formulates a multidimensional belief measure, which can quantify the known uncertainty and lower bound the unknown uncertainty of any given task. Our theoretical result establishes an important relationship between the conflicting belief and the incorrect belief. The theoretical result allows us to estimate the total uncertainty of a task, which provides a principled criterion for task selection. A novel multi-query task formulation is further developed to improve both the computational and labeling efficiency of meta-learning. Experiments conducted over multiple real-world few-shot image classification tasks demonstrate the effectiveness of the proposed model.

关键词： Training computer vision Uncertainty Computational modeling Measurement uncertainty Predictive models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Global Sensing and Measurements Reuse for Image Compressed Sensing

Global Sensing and Measurements Reuse for Image Compressed S...

引用

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

作者： Fan, Zi-En Lian, Feng Quan, Jia-Ni Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, existing methods obtain measurements only from partial features in the network and use it only once for image reconstruction. They ignore there are low, mid, and high-level features in the network [38] and all of them are essential for high-quality reconstruction. Moreover, using measurements only once may not be enough for extracting richer information from measurements. To address these issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all level features for achieving an efficient sensing and Measurements Reuse Block (MRB) to reuse measurements multiple times on multi-scale. Finally, we conduct a series of experiments on three benchmark datasets to show that our model can significantly outperform state-of-the-art methods.

关键词： GSM Convolutional codes computer vision Computational modeling Benchmark testing Feature extraction Sensors

来源：评论

学校读者我要写书评

暂无评论

Disentangling visual and written concepts in CLIP

Disentangling visual and written concepts in CLIP

引用

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

作者： Materzynska, Joanna Torralba, Antonio Bau, David MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The CLIP network measures the similarity between natural text and images;in this work, we investigate the entanglement of the representation of word images and natural images in its image encoder. First, we find that the image encoder has an ability to match word images with natural images of scenes described by those words. This is consistent with previous research that suggests that the meaning and the spelling of a word might be entangled deep within the network. On the other hand, we also find that CLIP has a strong ability to match nonsense words, suggesting that processing of letters is separated from processing of their meaning. To explicitly determine whether the spelling capability of CLIP is separable, we devise a procedure for identifying representation subspaces that selectively isolate or eliminate spelling capabilities. We benchmark our methods against a range of retrieval tasks, and we also test them by measuring the appearance of text in CLIP-guided generated images. We find that our methods are able to cleanly separate spelling capabilities of CLIP from the visual processing of natural images.

关键词： Visualization computer vision Image coding Benchmark testing Cognition pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Deep Hybrid Models for Out-of-Distribution Detection

Deep Hybrid Models for Out-of-Distribution Detection

引用

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

作者： Cao, Senqi Zhang, Zhongfei SUNY Binghamton Binghamton NY 13902 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a principled and practical method for outof-distribution (OoD) detection with deep hybrid models (DHMs), which model the joint density p(x, y) of features and labels with a single forward pass. By factorizing the joint density p(x, y) into three sources of uncertainty, we show that our approach has the ability to identify samples semantically different from the training data. To ensure computational scalability, we add a weight normalization step during training, which enables us to plug in state-of-the-art (SoTA) deep neural network (DNN) architectures for approximately modeling and inferring expressive probability distributions. Our method provides an efficient, general, and flexible framework for predictive uncertainty estimation with promising results and theoretical support. To our knowledge, this is the first work to reach 100% in OoD detection tasks on both vision and language datasets, especially on notably difficult dataset pairs such as CIFAR-10 vs. SVHN and CIFAR-100 vs. CIFAR-10. This work is a step towards enabling DNNs in real-world deployment for safety-critical applications.

关键词： Deep learning Training Uncertainty Statistical analysis Computational modeling Training data computer architecture

来源：评论

学校读者我要写书评

暂无评论

Simple Multi-dataset Detection

Simple Multi-dataset Detection

引用

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

作者： Zhou, Xingyi Koltun, Vladlen Krahenbuhl, Philipp Univ Texas Austin Austin TX 78712 USA Apple Cupertino CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span diverse datasets with potentially inconsistent taxonomies. In this paper, we present a simple method for training a unified detector on multiple large-scale datasets. We use dataset-specific training protocols and losses, but share a common detection architecture with dataset-specific outputs. We show how to automatically integrate these dataset-specific outputs into a common semantic taxonomy. In contrast to prior work, our approach does not require manual taxonomy reconciliation. Experiments show our learned taxonomy outperforms a expert-designed taxonomy in all datasets. Our multi-dataset detector performs as well as dataset-specific models on each training domain, and can generalize to new unseen dataset without fine-tuning on them. Code is available at https://***/xingyizhou/UniDet.

关键词： Training computer vision Protocols Taxonomy Semantics Detectors Object detection

来源：评论

学校读者我要写书评

暂无评论

Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain

Practical Learned Lossless JPEG Recompression with Multi-Lev...

引用

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

作者： Guo, Lina Shi, Xinjie He, Dailan Wang, Yuanyuan Ma, Rui Qin, Hongwei Wang, Yan Sense Time Res Hong Kong Peoples R China Natl Univ Def Technol Changsha Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

JPEG is a popular image compression method widely used by individuals, data center;cloud storage and network filesystems. However, most recent progress on image compression mainly focuses on uncompressed images while ignoring trillions of already-existing JPEG images. To compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, we propose a deep learning based JPEG recompression method that operates on DCT domain and propose a Multi-Level Cross-Channel Entropy Model to compress the most informative Y component. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this is the first learned compression method that losslessly transcodes JPEG images to more storage-saving bitstreams.

关键词： Deep learning Data centers computer vision Cloud computing Image coding Transform coding Entropy

来源：评论

学校读者我要写书评

暂无评论

Implicit Feature Decoupling with Depthwise Quantization

Implicit Feature Decoupling with Depthwise Quantization

引用

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

作者： Fostiropoulos, Iordanis Boehm, Barry Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Quantization has been applied to multiple domains in Deep Neural Networks (DNNs). We propose Depthwise Quantization (DQ) where quantization is applied to a decomposed sub-tensor along the feature axis of weak statistical dependence. The feature decomposition leads to an exponential increase in representation capacity with a linear increase in memory and parameter cost. In addition, DQ can be directly applied to existing encoder-decoder frameworks without modification of the DNN architecture. We use DQ in the context of Hierarchical Auto-Encoders and train end-to-end on an image feature representation. We provide an analysis of the cross-correlation between spatial and channel features and propose a decomposition of the image feature representation along the channel axis. The improved performance of the depthwise operator is due to the increased representation capacity from implicit feature decoupling. We evaluate DQ on the likelihood estimation task, where it outperforms the previous state-of-the-art on CIFAR-10, ImageNet-32 and ImageNet-64. We progressively train with increasing image size a single hierarchical model that uses 69% fewer parameters and has faster convergence than the previous work.

关键词： computer vision Quantization (signal) Tensors Statistical analysis Redundancy Neural networks Estimation

来源：评论

学校读者我要写书评

暂无评论

Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees

Pushing the Envelope of Gradient Boosting Forests via Global...

引用

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

作者： Gabidolla, Magzhan Carreira-Perpinan, Miguel A. Univ Calif Merced Dept CSE Merced CA 95343 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Ensemble methods based on decision trees, such as Random Forests or boosted forests, have long been established as some of the most powerful, off-the-shelf machine learning models, and have been widely used in computer vision and other areas. In recent years, a specific form of boosting, gradient boosting (GB), has gained prominence. This is partly because of highly optimized implementations such as XGBoost or LightGBM, which incorporate many clever modifications and heuristics. However, one gaping hole remains unexplored in GB: the construction of individual trees. To date, all successful GB versions use axis-aligned trees trained in a suboptimal way via greedy recursive partitioning. We address this gap by using a more powerful type of trees (having hyperplane splits) and an algorithm that can optimize, globally over all the tree parameters, the objective function that GB dictates. We show, in several benchmarks of image and other data types, that GB forests of these stronger, well-optimized trees consistently exceed the test accuracy of axis-aligned forests from XGBoost, LightGBM and other strong baselines. Further, this happens using many fewer trees and sometimes even fewer parameters overall.

关键词： computer vision Computational modeling Forestry Boosting Linear programming pattern recognition Partitioning algorithms

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 << < 160 161 162 163 164 165 166 167 168 169 > >>

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：