2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)最新文献_第5页

Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras 基于二部匹配的公交监控摄像机客流估计

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00038

Shunta Komatsu, Ryosuke Furuta, Y. Taniguchi

{"title":"Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras","authors":"Shunta Komatsu, Ryosuke Furuta, Y. Taniguchi","doi":"10.1109/MIPR51284.2021.00038","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00038","url":null,"abstract":"To formulate the schedules and routes of buses, bus companies monitor and gather data on the number of passengers and the boarding sections for each passenger several days a year. The problem is, however, that this monitoring is currently performed manually and requires a great deal of human cost. To solve this problem, recent proposals analyze the images taken by the surveillance cameras installed in most modern Japanese buses. The previous methods make it possible to identify the boarding sections regardless of the payment method like IC cards by matching people in the images obtained from different surveillance cameras. In this paper, we propose an improved method for estimating boarding sections; it uses minimum weight perfect matching on a bipartite graph; the assumption is that there exists one-to-one correspondence between people appearing in two surveillance camera images. In addition, the proposed method takes the boarding direction estimates output by person detection and tracking into account. To further improve the estimation accuracy, we employ a time constraint to handle the restricted movement of passengers on a bus. To confirm the effectiveness of the proposed method, we conduct experiments on the images taken by actual bus surveillance cameras. The results show that the proposed method achieves significantly better results than the previous method.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117098935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Fact-checking Assistant System for Textual Documents* 文本文件事实核查助理系统*

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00046

Tomoya Furuta, Yumiko Suzuki

引用次数: 1

TLV-Bandit: Bandit Method for Collecting Topic-related Local Tweets TLV-Bandit:收集与主题相关的本地tweet的土匪方法

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00016

Carina Miwa Yoshimura, H. Kitagawa

{"title":"TLV-Bandit: Bandit Method for Collecting Topic-related Local Tweets","authors":"Carina Miwa Yoshimura, H. Kitagawa","doi":"10.1109/MIPR51284.2021.00016","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00016","url":null,"abstract":"Twitter hosts a large and diverse amount of information that makes up a corpus of data valuable to a wide range of institutions from marketing firms to governments. Collection of tweets can enable analysis like surveys of public opinions, marketing analysis or target analysis to users who live in a specific area. To collect useful data for a given task, the ability to capture tweets related to a specific topic sent from a specific area is needed. However, performing this kind of task on significantly sizable data sources such as the twitter stream data using just the Twitter API is a big challenge because of limitation relating to usage restrictions and lack of geotags. In this work, we propose \"TLV-Bandit\", which collects topic-related tweets sent from a specific area based on the bandit algorithm and analyze its performance. The experimental results show that our proposed method can collect efficiently the target tweets in comparison to other methods when considering the three aspects of collection requirements: Locality (sent from the target area), Similarity (topic-related) and Volume (number of tweets).","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132610253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression 基于自适应秩惩罚的学习张量分解在cnn压缩中的应用

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00014

Deli Yu, Peipei Yang, Cheng-Lin Liu

{"title":"Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression","authors":"Deli Yu, Peipei Yang, Cheng-Lin Liu","doi":"10.1109/MIPR51284.2021.00014","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00014","url":null,"abstract":"Low-rank tensor decomposition is a widely-used strategy to compress convolutional neural networks (CNNs). Existing learning-based decomposition methods encourage low-rank filter weights via regularizer of filters’ pair-wise force or nuclear norm during training. However, these methods can not obtain the satisfactory low-rank structure. We propose a new method with an adaptive rank penalty to learn more compact CNNs. Specifically, we transform rank constraint into a differentiable one and impose its adaptive violation-aware penalty on filters. Moreover, this paper is the first work to integrate the learning-based decomposition and group decomposition to make a better trade-off, especially for the tough task of compression of 1×1 convolution.The obtained low-rank model can be easily decomposed while nearly keeping the full accuracy without additional fine-tuning process. The effectiveness is verified by compression experiments of VGG and ResNet on CIFAR-10 and ILSVRC-2012. Our method can reduce about 65% parameters of ResNet-110 with 0.04% Top-1 accuracy drop on CIFAR-10, and reduce about 60% parameters of ResNet-50 with 0.57% Top-1 accuracy drop on ILSVRC-2012.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128078325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Predicting inquiry from potential renters using property listing information 利用房源信息预测潜在租客的问询

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00053

Takeshi So, Y. Arai

引用次数: 0

Identifying Maturity Rating Levels of Online Books 确定在线图书的成熟度等级

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00032

Eric Brewer, Yiu-Kai Ng

{"title":"Identifying Maturity Rating Levels of Online Books","authors":"Eric Brewer, Yiu-Kai Ng","doi":"10.1109/MIPR51284.2021.00032","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00032","url":null,"abstract":"With the huge amount of books available nowadays, it is a challenge to determine appropriate reading materials that are suitable for a reader, especially books that match the maturity levels of children and adolescents. Analyzing the age-appropriateness for books can be a time-consuming process, since it can take up to three hours for a human to read a book, and the relatively low cost of creating literary content can cause it to be even more difficult to discover age-suitable materials to read. In order to solve this problem, we propose a maturity-rating-level detection tool based on neural network models. The proposed model predicts a book’s content rating level within each of the seven categories: (i) crude humor/language; (ii) drug, alcohol, and tobacco use; (iii) kissing; (iv) profanity; (v) nudity; (vi) sex and intimacy; and (vii) violence and horror, given the text of the book. The empirical study demonstrates that mature content of online books can be accurately predicted by computers through the use of natural language processing and machine learning techniques. Experimental results also verify the merit of the proposed model that outperforms a number of baseline models and well-known, existing maturity ratings prediction tools.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131264851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Pop’n Food: 3D Food Model Estimation System from a Single Image 流行食品:从单个图像的3D食品模型估计系统

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00041

Shu Naritomi, Keiji Yanai

{"title":"Pop’n Food: 3D Food Model Estimation System from a Single Image","authors":"Shu Naritomi, Keiji Yanai","doi":"10.1109/MIPR51284.2021.00041","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00041","url":null,"abstract":"Dietary calorie management has been an important topic in recent years, and various methods and applications on image-based food calorie estimation have been published in the multimedia community. Most of the existing methods of estimating food calorie amounts use 2D-based image recognition. However, since actual food is a 3D object, there is a limit to the accuracy of calorie estimation using 2D-based methods. Therefore, in our previous work, we proposed a method to reconstruct the 3D shape of the dish (food and plate) and a plate (without foods) from a single 2D image and estimate a more accurate food volume. Such researches on 3D reconstruction have been active recently, and it is necessary to qualitatively evaluate what kind of 3D shape has been reconstructed. However, checking a large number of 3D models reconstructed from a large number of images requires many steps and is tedious. Against this background, this demo paper introduces an application named \"Pop’n Food\" which has the following two functions: (1) A web application for visualizing a large number of images to check the learning results and the 3D model generated from them. (2) A web application that selects an image from a browser and generates and visualizes a 3D model in real-time. This demo system is based on our previous work named Hungry Networks. Demo video: https://youtu.be/YyIu8bL65EE","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Entity Resolution of Japanese Apartment Property Information Using Neural Networks 基于神经网络的日本公寓物业信息实体解析

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00052

Y. Kado, Takashi Hirokata, Koji Matsumura, Xueting Wang, T. Yamasaki

{"title":"Entity Resolution of Japanese Apartment Property Information Using Neural Networks","authors":"Y. Kado, Takashi Hirokata, Koji Matsumura, Xueting Wang, T. Yamasaki","doi":"10.1109/MIPR51284.2021.00052","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00052","url":null,"abstract":"In Japan, there are many real estate companies and agencies, who create apartment room property records and register them to some real estate portal sites to be advertised. The apartment room records include the apartment building attributes information. However, the building attributes values are not entered by referring to the common building database but are arbitrarily created and entered by each company or agency. For effective use of property information, apartment rooms must be linked to the correct apartment building. In this regard, aggregating property information belonging to the same building (entity resolution) is typically performed by a rule-based process that statistically considers the similarity of attributes such as the building name, number of floors, or year/month the building was built. However, when property information is stored by room and registered by different businesses, the corresponding building information may be inconsistent, incomplete, or inaccurate. Therefore, entity resolution using a rule-based method is insufficient and requires extensive manual post-processing. This study proposes an entity resolution method for apartment properties using neural networks with inputs containing traditional property attributes and new attributes obtained from the phonetic and semantic pre-processing of building names. The experimental results show that the proposed method improves entity resolution accuracy.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117214568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demo Paper: Ad Hoc Search On Statistical Data Based On Categorization And Metadata Augmentation 演示论文:基于分类和元数据扩充的统计数据的特别搜索

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00043

T. Okamoto, H. Miyamori

引用次数: 0

Respective Volumetric Heatmap Autoencoder for Multi-Person 3D Pose Estimation 各自的体积热图自动编码器的多人三维姿态估计

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00070

Minghao Wang, Long Ye, Fei Hu, Li Fang, Wei Zhong, Qin Zhang

引用次数: 0