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

73 篇 会议
36 篇 期刊文献
1 册 图书

馆藏范围

110 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

73 篇 理学
- 51 篇 数学
- 23 篇 生物学
- 20 篇 物理学
- 18 篇 统计学（可授理学、...
- 6 篇 化学
- 4 篇 系统科学
67 篇 工学
- 53 篇 计算机科学与技术...
- 51 篇 软件工程
- 28 篇 信息与通信工程
- 22 篇 生物工程
- 8 篇 机械工程
- 6 篇 光学工程
- 5 篇 化学工程与技术
- 5 篇 生物医学工程（可授...
- 3 篇 交通运输工程
- 3 篇 安全科学与工程
- 2 篇 冶金工程
- 1 篇 仪器科学与技术
- 1 篇 电气工程
- 1 篇 控制科学与工程
- 1 篇 石油与天然气工程
- 1 篇 轻工技术与工程
- 1 篇 核科学与技术
23 篇 管理学
- 15 篇 图书情报与档案管...
- 7 篇 管理科学与工程(可...
- 2 篇 工商管理
2 篇 法学
- 2 篇 社会学
1 篇 经济学
- 1 篇 理论经济学
- 1 篇 应用经济学
1 篇 教育学
- 1 篇 教育学
1 篇 农学

主题

13 篇 emotion recognit...
6 篇 face recognition
4 篇 financial market...
4 篇 neural networks
4 篇 feature extracti...
4 篇 training
4 篇 convolutional ne...
3 篇 deep neural netw...
3 篇 random variables
3 篇 graphic methods
3 篇 image recognitio...
2 篇 object detection
2 篇 task analysis
2 篇 trees (mathemati...
2 篇 automation
2 篇 recommender syst...
2 篇 self-supervised ...
2 篇 convolution
2 篇 factorization
2 篇 predictive model...

机构

23 篇 laboratory of al...
23 篇 hse university l...
15 篇 sber ai lab mosc...
13 篇 laboratory of al...
8 篇 national researc...
7 篇 hse university n...
6 篇 national researc...
5 篇 laboratory of al...
3 篇 lobachevsky stat...
3 篇 moscow
3 篇 laboratory of di...
3 篇 national researc...
3 篇 laboratory of al...
3 篇 department of in...
2 篇 laboratory of al...
2 篇 laboratory of al...
2 篇 st. petersburg d...
2 篇 isp ras research...
2 篇 national researc...
2 篇 laboratory of al...

作者

35 篇 savchenko andrey...
9 篇 andrey v. savche...
9 篇 savchenko andrey
8 篇 batsyn mikhail
6 篇 bychkov ilya
5 篇 pardalos panos m...
4 篇 gribanov dmitry
4 篇 koldanov p.a.
4 篇 andrey savchenko
3 篇 pardalos panos
3 篇 koldanov a.p.
3 篇 kalyagin valeriy
3 篇 koldanov petr a.
3 篇 shumilov ivan
3 篇 savchenko a.v.
3 篇 makarov ilya
3 篇 grechikhin ivan ...
3 篇 malyshev dmitry
3 篇 utkina irina
3 篇 demochkina polin...

语言

107 篇 英文
3 篇 其他
1 篇 中文

检索条件"机构=Laboratory of Algorithms and Technologies for Network Analysis"

共 110 条记录，以下是21-30 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：

Group-Level Affect Recognition in Video Using Deviation of Frame Features 10th

Group-Level Affect Recognition in Video Using Deviation of...

引用

10th International Conference on analysis of Images, Social networks and Texts, AIST 2021

作者： Savchenko, Andrey V. Savchenko, Lyudmila V. Belova, Natalya S. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia HSE University Moscow Russia

ISBN: (纸本)9783031164996

In this paper, we propose the novel video-based group-level emotion recognition algorithm. At first, the faces are detected in each video frame, and their features are extracted using a lightweight neural network, e.g., MobileNet pre-trained on large emotional dataset, such as AffectNet. The frame descriptor is defined as a concatenation of STAT features (max, average, standard deviation, etc.). The descriptor of the whole video is computed as a deviation of the frame descriptors, and the resulting video features are fed into a classifier. Experimental results for the VGAF dataset from the EmotiW 2020 challenge demonstrate that the proposed approach has 1% greater accuracy than the best-known single model. It is also at least 5% better than any other facial processing technique. Moreover, a blending of facial expression recognition with a processing of audio features extracted by the OpenSMILE library is comparable with the best-known ensemble for this dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Group-Level emotion recognition using transfer learning from face identification 17

Group-Level emotion recognition using transfer learning from...

引用

19th ACM International Conference on Multimodal Interaction, ICMI 2017

作者： Rassadin, Alexandr Gruzdev, Alexey Savchenko, Andrey National Research University Laboratory of Algorithms and Technologies for Network Analysis Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9781450355438

In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition subchallenge. We extracted feature vectors of detected faces using the Convolutional Neural network trained for face identification task, rather than traditional pre-Training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available. © 2017 ACM.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Visual Product Recommendation Using Neural Aggregation network and Context Gating 8th

Visual Product Recommendation Using Neural Aggregation Netwo...

引用

8th International Conference on network analysis, NET 2018

作者： Demochkin, Kirill V. Savchenko, Andrey V. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9783030371562

In this paper, we focus on the problem of user interests’ classification in visual product recommender systems. We propose a two-stage procedure. At first, the image features are learned by fine-tuning the convolutional neural network, e.g., MobileNet. In the second stage, we use such learnable pooling techniques as neural aggregation network and context gating in order to compute a weighted average of image features. As a result, we can capture the relationships between the products’ images purchased by the same user. We provide an experimental study with the Amazon product dataset. It was shown that our approach achieves an F1-measure of 0.90 for 15 recommendations, which is much higher when compared to 0.66 F1-measure classifications of traditional averaging of the feature vector. © Springer Nature Switzerland AG 2020.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Uncertainty of efficient frontier in portfolio optimization 14th

Uncertainty of efficient frontier in portfolio optimization

引用

14th International Conference on Learning and Intelligent Optimization, LION 2020

作者： Kalygin, Valery A. Slashchinin, Sergey V. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9783030535513

Portfolio optimization is a large area of investigation both in theoretical and practical setting since the seminal work by Markowitz where a mean-variance model was introduced. From optimization point of view, the problem of optimal portfolio in mean-variance setting can be formulated as convex quadratic optimization under uncertainty. In practice one needs to estimate parameters of the model to find an optimal portfolio. Error in the parameter estimation generates error in the optimal portfolio. It was observed that the out of sample behavior of obtained solution is not in accordance with what is expected. Main reason for this phenomena is related with the estimation of means of stock returns, estimation of covariance matrix being less important. In the present paper we study uncertainty of identification of efficient frontier (Pareto optimal portfolios) in mean-variance model. In order to avoid the estimation of means of returns we use CVaR optimization method by Rockafellar and Uryasev. First we prove, that for a large class of elliptical distributions efficient frontier in mean-variance model is identical to the trajectory of CVaR optimal portfolios with the change of the confidence level. This gives an alternative way to recover efficient frontier in mean-variance model. Next we conduct a series of numerical experiments to test the proposed approach. We show that proposed approach is competitive with existing methods. © Springer Nature Switzerland AG 2020.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

EmotiEffNets for Facial Processing in Video-based Valence-Arousal Prediction, Expression Classification and Action Unit Detection

EmotiEffNets for Facial Processing in Video-based Valence-Ar...

引用

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Savchenko, Andrey V. Sber Ai Lab Moscow Russia Hse University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9798350302493

In this article, the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction are used for downstream emotion analysis tasks from the fifth Affective Behavior analysis in-the-wild (ABAW) competition. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model is much better than the baseline facial processing using VGGFace And ResNet. For example, our macro-averaged F1-scores of facial expression recognition and action unit detection on the testing set are 11-13% greater. Moreover, the concordance correlation coefficients for valence/arousal estimation are up to 30% higher when compared to the baseline. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Summarization of Videos from Online Events Based on Multimodal Emotion Recognition

Summarization of Videos from Online Events Based on Multimod...

引用

2022 International Russian Automation Conference, RusAutoCon 2022

作者： Abdrahimov, Amir Savchenko, Andrey V. Hse University Nizhny Novgorod Russia Hse University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

In this paper, we propose a novel video summarization technique for automatic affect analysis of participants of an online event. At first, face verification neural network is used to cluster facial regions that correspond to each participant. Next, emotional features are extracted from each face by using EfficientNet model obtained in the previous paper of the author. The features of several consecutive frames are combined into a single descriptor that is used to classify emotions. In addition, audio features are extracted using wav2vec, and an ensemble of audio and video classifiers predicts emotions for each face. Finally, dependence of these emotions on time is visualized in special color charts. In the experimental study with the AFEW dataset it was demonstrated that the proposed approach makes it possible to obtain the best-known validation accuracy 67.88%. The models were optimized using OpenVINO and gained reasonable performance even if Nvidia GPUs are unavailable. The models and source code are publicly available at https://***/amirabdrahimov/multimodal-emotion-recognition. © 2022 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Stability testing of stock returns connections 1

引用

6th International Conference on network analysis, NET 2016

作者： Voronina, M.A. Koldanov, P.A. National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Moscow Russia

ISBN: (数字)9783319568294

ISBN: (纸本)9783319568287

The problem of stability of connections of stock returns over time is considered. This problem is formulated as a multiple testing problem of homogeneity of covariance matrices. A statistical procedure based on Box’s M-test and Bonferroni correction is proposed. This procedure is applied to French and German stock markets. © Springer International Publishing AG 2017.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

Neural network model for video-based facial expression recognition in-the-wild on mobile devices 7

Neural network model for video-based facial expression recog...

引用

7th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2021

作者： Demochkina, Polina Savchenko, Andrey V. HSE University Nizhny Novgorod Russia HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9781665432177

In this paper, we propose to solve the problem of facial expression recognition in videos by implementing a two-stage procedure, in which, firstly, facial features are extracted from all frames using an EfficientNet-based model. The latter is pre-trained to identify facial attributes and further fine-tuned on an external dataset for the emotion classification task. Secondly, multiple statistical functions are calculated and used in the aggregation process to create a single video representation. Furthermore, we propose a new technique for sequence, frame-level attention models, and 1D convolutions by concatenating the output of a statistical function with the facial features. It was experimentally shown that the proposed approach leads to state-of-the-art results on the AFEW 8.0 dataset. © 2021 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

MT-EmotiEffNet for Multi-task Human Affective Behavior analysis and Learning from Synthetic Data 17th

MT-EmotiEffNet for Multi-task Human Affective Behavior Anal...

引用

17th European Conference on Computer Vision, ECCV 2022

作者： Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia Sber AI Lab Moscow Russia

ISBN: (纸本)9783031250743

In this paper, we present the novel multi-task EfficientNet model and its usage in the 4th competition on Affective Behavior analysis in-the-wild (ABAW). This model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward neural networks in the multi-task learning challenge. We obtain performance measure 1.3 on the validation set, which is significantly greater when compared to either performance of baseline (0.3) or existing models that are trained only on the s-Aff-Wild2 database. In the learning from synthetic data challenge, the quality of the original synthetic training set is increased by using the super-resolution techniques, such as Real-ESRGAN. Next, the MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our average validation F1 score is 18% greater than the baseline model. As a result, our team took the first place in the learning from synthetic data task and the third place in the multi-task learning challenge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blending

来源：评论

学校读者我要写书评

暂无评论

Touching the Limits of a Dataset in Video-Based Facial Expression Recognition

Touching the Limits of a Dataset in Video-Based Facial Expre...

引用

2021 International Russian Automation Conference, RusAutoCon 2021

作者： Churaev, Egor Savchenko, Andrey V. Hse University Nizhny Novgorod Russia Hse University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9780738131566

In this paper, we examine the issue of video-based facial emotion recognition algorithms which show excellent performance on some benchmarks, but have much worse accuracy in practical applications. For example, the typical error rate of contemporary deep neural networks on the RAVDESS dataset is less than 5%. We argue that such results are obtained only if the split of the whole dataset is incorrect, so that the same persons are present in both training and test sets. It is claimed that it is more frankly to use the actor-based split, in which persons in the training and test sets are disjoint. It is experimentally demonstrated that the near state-of-the-art neural network model pre-trained on the AffectNet dataset achieves 99% accuracy on conventional split of the RAVDESS dataset. However, when we split the dataset by the actors and training and testing sets have only unique persons then the accuracy will be 20-30% lower. © 2021 IEEE.

关键词： Emotion Recognition

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11页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：