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

Are Theme Songs Usable for Anime Retrieval? 动画检索是否可以使用主题曲?

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00042

Naoto Homma, Aiko Uemura, Tetsuro Kitahara

引用次数: 0

FPX-G: First Person Exploration for Graph FPX-G:第一人称探索图

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00018

Takahiro Komamizu, Shoi Ito, Yasuhiro Ogawa, K. Toyama

{"title":"FPX-G: First Person Exploration for Graph","authors":"Takahiro Komamizu, Shoi Ito, Yasuhiro Ogawa, K. Toyama","doi":"10.1109/MIPR51284.2021.00018","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00018","url":null,"abstract":"Data exploration is a fundamental user task in the information seeking process. In data exploration, users have ambiguous information needs, and they traverse across the data for gathering information. In this paper, a novel data exploration system, called FPX-G, is proposed that uses virtual reality (VR) technology. VR-based data exploration (or immersive analytics) is a recent trend in data analytics, and the existing work approaches involve aggregated information in an interactive and 3D manner. However, exploration for individual pieces of data scarcely has been approached. Traditional data exploration is done on 2D displays, therefore space is limited, and there is no depth. FPX-G fully utilizes 3D space to make individual piece of data visible in the user’s line of sight. In this paper, the data structure in FPX-G is designed as a graph, and the data exploration process is modeled as graph traversal. To utilize the capability of VR, FPX-G provides a first person view-based interface from which users can look at individual pieces of data and can walk through the data (like walking in a library). In addition to the walking mechanism, to deal with limited physical space in a room, FPX-G introduces eye-tracking technology for traversing data through a graph. A simulation-based evaluation reveals that FPX-G provides a significantly efficient interface for exploring data compared with the traditional 2D interface.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"72 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":"130351851","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

Topic Detection for Video Stream based on Geographical Relationships and its Interactive Viewing System 基于地理关系的视频流主题检测及其交互观看系统

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00012

Itsuki Hashimoto, Yuanyuan Wang, Yukiko Kawai, K. Sumiya

{"title":"Topic Detection for Video Stream based on Geographical Relationships and its Interactive Viewing System","authors":"Itsuki Hashimoto, Yuanyuan Wang, Yukiko Kawai, K. Sumiya","doi":"10.1109/MIPR51284.2021.00012","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00012","url":null,"abstract":"During the recent years of Internet TV spread, researches on recommending relevant information for TV programs have been actively conducted. The NHK’s Hybridcast provides a service that recommends relevant information on the same screen during the broadcast of a TV program. However, there is currently no service for recommending supplementary information based on the users’ viewing behavior. Based on this research background, we first extract geographic words (location names) and topics of each scene using closed captions of TV programs. Next, we analyze the user’s viewing behavior to extract the scenes selected by the user in the sequence. After that, we can detect the topics of the user’s selected scenes. Therefore, the supplementary information is recommended by generating queries based on geographical relationships using geographical words and topics. In this paper, we discuss our proposed system for supporting interactive viewing of TV programs, which is based on the viewing behavior of users and geographic relationships.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"9 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":"128080820","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

Exploring the Spatial-Visual Locality of Geo-tagged Urban Street Images 城市街道图像地理标记的空间视觉局部性研究

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00023

Abdullah Alfarrarjeh, Xiao Yang, A. A. Jabal, S. H. Kim, C. Shahabi

引用次数: 0

Dance to Music: Generative Choreography with Music using Mixture Density Networks 随着音乐起舞:使用混合密度网络的音乐生成编舞

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00065

Rongfeng Li, Meng Zhao, Xianlin Zhang, Xueming Li

{"title":"Dance to Music: Generative Choreography with Music using Mixture Density Networks","authors":"Rongfeng Li, Meng Zhao, Xianlin Zhang, Xueming Li","doi":"10.1109/MIPR51284.2021.00065","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00065","url":null,"abstract":"Choreography is usually done by professional choreographers, while the development of motion capture technology and artificial intelligence has made it possible for computers to choreograph with music. There are two main challenges in choreography: 1) how to get real and novel dance moves without relying on motion capture and manual production, and 2) how to use the appropriate music and motion features and matching algorithms to enhance the synchronization of music and dance. Focusing on these two targets above, we propose a framework based on Mixture Density Network (MDN) to synthesis dances that match the target music. The framework includes three steps: motion generation, motion screening and feature matching. In order to make the dance movements generated by the model applicable for choreography with music, we propose a parameter control algorithm and a coherence-based motion screening algorithm to improve the consistency of dance movements. Moreover, to achieve better unity of music and motions, we propose a multi-level music and motion feature matching algorithm, which combines global feature matching with local feature matching. Finally, our framework proved to be able to synthesis more coherent and creative choreography with music.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"63 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":"115766441","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

Augmented Tai-Chi Chuan Practice Tool with Pose Evaluation 增强太极拳练习工具与姿势评估

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00013

Y. Jan, Kuan-Wei Tseng, Peng-Yuan Kao, Y. Hung

引用次数: 1

Multimodal Machine Translation Enhancement by Fusing Multimodal-attention and Fine-grained Image Features 融合多模态注意和细粒度图像特征的多模态机器翻译增强

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00050

Lin Li, Turghun Tayir

{"title":"Multimodal Machine Translation Enhancement by Fusing Multimodal-attention and Fine-grained Image Features","authors":"Lin Li, Turghun Tayir","doi":"10.1109/MIPR51284.2021.00050","DOIUrl":"https://doi.org/10.1109/MIPR51284.2021.00050","url":null,"abstract":"With recent development of the multimodal machine translation (MMT) network architectures, recurrent models have effectively been replaced by attention mechanism and the translation results have been enhanced with the assistance of fine-grained image information. Although attention is a powerful and ubiquitous mechanism, different number of attention heads and granularity image features aligned by attention have an impact on the quality of multimodal machine translation. In order to address above problems, this paper proposes a multimodal machine translation enhancement by fusing multimodal-attention and fine-grained image features method which builds some submodels by introducing different granularity of image features to the multimodal-attention mechanism with different number of heads. Moreover, these sub-models are randomly fused and fusion models are obtained. The experimental results on the Multi30k dataset that the pruned attention heads lead to the improvement of translation results. Finally, our fusion model obtained the best results according to the automatic evaluation metrics BLEU compared with sub-models and some baselines.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"43 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":"126693184","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

Layout Structure Assisted Indoor Image Generation 布局结构辅助室内图像生成

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00061

Zhijie Qin, Wei Zhong, Fei Hu, Xinyan Yang, Long Ye, Qin Zhang

引用次数: 0

Text Style Transfer With Decorative Elements 文字风格转移与装饰元素

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00062

Yuting Ma, Fan Tang, Weiming Dong, Changsheng Xu

引用次数: 0

Clustering Trajectories via Sparse Auto-encoders 基于稀疏自编码器的聚类轨迹

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2021-09-01 DOI: 10.1109/MIPR51284.2021.00049

Xiaofeng Wu, Rui Zhang, Lin Li

引用次数: 0