{"title":"Session details: FF-3","authors":"Zhu Li","doi":"10.1145/3286925","DOIUrl":"https://doi.org/10.1145/3286925","url":null,"abstract":"","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122035688","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}
Klaus Schöffmann, W. Bailer, C. Gurrin, G. Awad, Jakub Lokoč
In this tutorial we discuss interactive video search tools and methods, review their need in the age of deep learning, and explore video and multimedia search challenges and their role as evaluation benchmarks in the field of multimedia information retrieval. We cover three different campaigns (TRECVID, Video Browser Showdown, and the Lifelog Search Challenge), discuss their goals and rules, and present their achieved findings over the last half-decade. Moreover, we talk about datasets, tasks, evaluation procedures, and examples of interactive video search tools, as well as how they evolved over the years. Participants of this tutorial will be able to gain collective insights from all three challenges and use them for focusing their research efforts on outstanding problems that still remain unsolved in this area.
{"title":"Interactive Video Search: Where is the User in the Age of Deep Learning?","authors":"Klaus Schöffmann, W. Bailer, C. Gurrin, G. Awad, Jakub Lokoč","doi":"10.1145/3240508.3241473","DOIUrl":"https://doi.org/10.1145/3240508.3241473","url":null,"abstract":"In this tutorial we discuss interactive video search tools and methods, review their need in the age of deep learning, and explore video and multimedia search challenges and their role as evaluation benchmarks in the field of multimedia information retrieval. We cover three different campaigns (TRECVID, Video Browser Showdown, and the Lifelog Search Challenge), discuss their goals and rules, and present their achieved findings over the last half-decade. Moreover, we talk about datasets, tasks, evaluation procedures, and examples of interactive video search tools, as well as how they evolved over the years. Participants of this tutorial will be able to gain collective insights from all three challenges and use them for focusing their research efforts on outstanding problems that still remain unsolved in this area.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123921380","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}
Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.
{"title":"Generative Adversarial Product Quantisation","authors":"Litao Yu, Yongsheng Gao, J. Zhou","doi":"10.1145/3240508.3240590","DOIUrl":"https://doi.org/10.1145/3240508.3240590","url":null,"abstract":"Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127255085","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}
In the artwork, the topic of flowing human figures has been discussed. People pass through familiar places day by day, in which they create connection among them and the city. The impressions, memories and experiences turn the definition of the space in the city into place, and it is meaningful and creates a virtual layer upon the physical world. The artwork tried to arouse people to aware the connection among them and the environment by revealing the invisible traces. The interactive installation was set in outdoor exhibition, and a camera was set align the road and a projector was used for performing image on the wall of the nearby building. Object detection technology has been used in the interactive installation for capturing movements of people. GMM modeling was adopted for capturing frames with vivid face features, and the parameters was set for generating afterimage effect. The projected picture on the wall combined with 25 frames in different update time setting for performing a delayed vision, and only one region in the center of the image played the current frame in real-time, for arousing audience to notice the connection between their movements and the projected picture. In addition, some of them were reversed in horizontal direction for creating a dynamic Chinese brush painting with aesthetic composition. The remaining figures on the wall as mark or print remind people their traces in the city, and that creates the connection among the city and people who has been to the place at the same time. In the interactive installation, the improvisational painting of body calligraphy was exhibited in a collaborative way, in which revealed the face features or human shapes of the crowd in physical point, and also the collaborative experiences or memories in mental aspect.
{"title":"Shadow Calligraphy of Dance: An Image-Based Interactive Installation for Capturing Flowing Human Figures","authors":"Lyn Chao-ling Chen, He-Lin Luo","doi":"10.1145/3240508.3264576","DOIUrl":"https://doi.org/10.1145/3240508.3264576","url":null,"abstract":"In the artwork, the topic of flowing human figures has been discussed. People pass through familiar places day by day, in which they create connection among them and the city. The impressions, memories and experiences turn the definition of the space in the city into place, and it is meaningful and creates a virtual layer upon the physical world. The artwork tried to arouse people to aware the connection among them and the environment by revealing the invisible traces. The interactive installation was set in outdoor exhibition, and a camera was set align the road and a projector was used for performing image on the wall of the nearby building. Object detection technology has been used in the interactive installation for capturing movements of people. GMM modeling was adopted for capturing frames with vivid face features, and the parameters was set for generating afterimage effect. The projected picture on the wall combined with 25 frames in different update time setting for performing a delayed vision, and only one region in the center of the image played the current frame in real-time, for arousing audience to notice the connection between their movements and the projected picture. In addition, some of them were reversed in horizontal direction for creating a dynamic Chinese brush painting with aesthetic composition. The remaining figures on the wall as mark or print remind people their traces in the city, and that creates the connection among the city and people who has been to the place at the same time. In the interactive installation, the improvisational painting of body calligraphy was exhibited in a collaborative way, in which revealed the face features or human shapes of the crowd in physical point, and also the collaborative experiences or memories in mental aspect.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129928933","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}
Qing Zhang, Ganzhao Yuan, Chunxia Xiao, Lei Zhu, Weishi Zheng
We address the problem of correcting the exposure of underexposed photos. Previous methods have tackled this problem from many different perspectives and achieved remarkable progress. However, they usually fail to produce natural-looking results due to the existence of visual artifacts such as color distortion, loss of detail, exposure inconsistency, etc. We find that the main reason why existing methods induce these artifacts is because they break a perceptually similarity between the input and output. Based on this observation, an effective criterion, termed as perceptually bidirectional similarity (PBS) is proposed. Based on this criterion and the Retinex theory, we cast the exposure correction problem as an illumination estimation optimization, where PBS is defined as three constraints for estimating illumination that can generate the desired result with even exposure, vivid color and clear textures. Qualitative and quantitative comparisons, and the user study demonstrate the superiority of our method over the state-of-the-art methods.
{"title":"High-Quality Exposure Correction of Underexposed Photos","authors":"Qing Zhang, Ganzhao Yuan, Chunxia Xiao, Lei Zhu, Weishi Zheng","doi":"10.1145/3240508.3240595","DOIUrl":"https://doi.org/10.1145/3240508.3240595","url":null,"abstract":"We address the problem of correcting the exposure of underexposed photos. Previous methods have tackled this problem from many different perspectives and achieved remarkable progress. However, they usually fail to produce natural-looking results due to the existence of visual artifacts such as color distortion, loss of detail, exposure inconsistency, etc. We find that the main reason why existing methods induce these artifacts is because they break a perceptually similarity between the input and output. Based on this observation, an effective criterion, termed as perceptually bidirectional similarity (PBS) is proposed. Based on this criterion and the Retinex theory, we cast the exposure correction problem as an illumination estimation optimization, where PBS is defined as three constraints for estimating illumination that can generate the desired result with even exposure, vivid color and clear textures. Qualitative and quantitative comparisons, and the user study demonstrate the superiority of our method over the state-of-the-art methods.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130063777","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}
Paula Gómez Duran, Eva Mohedano, Kevin McGuinness, Xavier Giro-i-Nieto, N. O’Connor
Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies connection with any underlying CBIR system; and a server that manages the search engine data. The software itself is described in a separate submission to the ACM MM Open Source Software Competition.
{"title":"Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems","authors":"Paula Gómez Duran, Eva Mohedano, Kevin McGuinness, Xavier Giro-i-Nieto, N. O’Connor","doi":"10.1145/3240508.3241395","DOIUrl":"https://doi.org/10.1145/3240508.3241395","url":null,"abstract":"Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies connection with any underlying CBIR system; and a server that manages the search engine data. The software itself is described in a separate submission to the ACM MM Open Source Software Competition.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129065536","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}
{"title":"Session details: Demo + Video + Makers' Program","authors":"K. Sohn, Yong Man Ro","doi":"10.1145/3286930","DOIUrl":"https://doi.org/10.1145/3286930","url":null,"abstract":"","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131028578","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}
Facial expression recognition (FER) is a very challenging problem due to different expressions under arbitrary poses. Most conventional approaches mainly perform FER under laboratory controlled environment. Different from existing methods, in this paper, we formulate the FER in the wild as a domain adaptation problem, and propose a novel auxiliary domain guided Cycle-consistent adversarial Attention Transfer model (CycleAT) for simultaneous facial image synthesis and facial expression recognition in the wild. The proposed model utilizes large-scale unlabeled web facial images as an auxiliary domain to reduce the gap between source domain and target domain based on generative adversarial networks (GAN) embedded with an effective attention transfer module, which enjoys several merits. First, the GAN-based method can automatically generate labeled facial images in the wild through harnessing information from labeled facial images in source domain and unlabeled web facial images in auxiliary domain. Second, the class-discriminative spatial attention maps from the classifier in source domain are leveraged to boost the performance of the classifier in target domain. Third, it can effectively preserve the structural consistency of local pixels and global attributes in the synthesized facial images through pixel cycle-consistency and discriminative loss. Quantitative and qualitative evaluations on two challenging in-the-wild datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.
{"title":"Facial Expression Recognition in the Wild: A Cycle-Consistent Adversarial Attention Transfer Approach","authors":"Feifei Zhang, Tianzhu Zhang, Qi-rong Mao, Ling-yu Duan, Changsheng Xu","doi":"10.1145/3240508.3240574","DOIUrl":"https://doi.org/10.1145/3240508.3240574","url":null,"abstract":"Facial expression recognition (FER) is a very challenging problem due to different expressions under arbitrary poses. Most conventional approaches mainly perform FER under laboratory controlled environment. Different from existing methods, in this paper, we formulate the FER in the wild as a domain adaptation problem, and propose a novel auxiliary domain guided Cycle-consistent adversarial Attention Transfer model (CycleAT) for simultaneous facial image synthesis and facial expression recognition in the wild. The proposed model utilizes large-scale unlabeled web facial images as an auxiliary domain to reduce the gap between source domain and target domain based on generative adversarial networks (GAN) embedded with an effective attention transfer module, which enjoys several merits. First, the GAN-based method can automatically generate labeled facial images in the wild through harnessing information from labeled facial images in source domain and unlabeled web facial images in auxiliary domain. Second, the class-discriminative spatial attention maps from the classifier in source domain are leveraged to boost the performance of the classifier in target domain. Third, it can effectively preserve the structural consistency of local pixels and global attributes in the synthesized facial images through pixel cycle-consistency and discriminative loss. Quantitative and qualitative evaluations on two challenging in-the-wild datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132833448","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}
Zhihe Lu, Tanhao Hu, Lingxiao Song, Zhaoxiang Zhang, R. He
Facial expression synthesis with various intensities is a challenging synthesis task due to large identity appearance variations and a paucity of efficient means for intensity measurement. This paper advances the expression synthesis domain by the introduction of a Couple-Agent Face Parsing based Generative Adversarial Network (CAFP-GAN) that unites the knowledge of facial semantic regions and controllable expression signals. Specially, we employ a face parsing map as a controllable condition to guide facial texture generation with a special expression, which can provide a semantic representation of every pixel of facial regions. Our method consists of two sub-networks: face parsing prediction network (FPPN) uses controllable labels (expression and intensity) to generate a face parsing map transformation that corresponds to the labels from the input neutral face, and facial expression synthesis network (FESN) makes the pretrained FPPN as a part of it to provide the face parsing map as a guidance for expression synthesis. To enhance the reality of results, couple-agent discriminators are served to distinguish fake-real pairs in both two sub-nets. Moreover, we only need the neutral face and the labels to synthesize the unknown expression with different intensities. Experimental results on three popular facial expression databases show that our method has the compelling ability on continuous expression synthesis.
{"title":"Conditional Expression Synthesis with Face Parsing Transformation","authors":"Zhihe Lu, Tanhao Hu, Lingxiao Song, Zhaoxiang Zhang, R. He","doi":"10.1145/3240508.3240647","DOIUrl":"https://doi.org/10.1145/3240508.3240647","url":null,"abstract":"Facial expression synthesis with various intensities is a challenging synthesis task due to large identity appearance variations and a paucity of efficient means for intensity measurement. This paper advances the expression synthesis domain by the introduction of a Couple-Agent Face Parsing based Generative Adversarial Network (CAFP-GAN) that unites the knowledge of facial semantic regions and controllable expression signals. Specially, we employ a face parsing map as a controllable condition to guide facial texture generation with a special expression, which can provide a semantic representation of every pixel of facial regions. Our method consists of two sub-networks: face parsing prediction network (FPPN) uses controllable labels (expression and intensity) to generate a face parsing map transformation that corresponds to the labels from the input neutral face, and facial expression synthesis network (FESN) makes the pretrained FPPN as a part of it to provide the face parsing map as a guidance for expression synthesis. To enhance the reality of results, couple-agent discriminators are served to distinguish fake-real pairs in both two sub-nets. Moreover, we only need the neutral face and the labels to synthesize the unknown expression with different intensities. Experimental results on three popular facial expression databases show that our method has the compelling ability on continuous expression synthesis.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132882885","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}
Learning to hash has proven to be an effective solution for indexing high-dimensional data by projecting them to similarity-preserving binary codes. However, most existing methods end up the learning scheme with a binarization stage, i.e. binary quantization, which inevitably destroys the neighborhood structure of original data. As a result, those methods still suffer from great similarity loss and result in unsatisfactory indexing performance. In this paper we propose a novel hashing model, namely Post Tuned Hashing (PTH), which includes a new post-tuning stage to refine the binary codes after binarization. The post-tuning seeks to rebuild the destroyed neighborhood structure, and hence significantly improves the indexing performance. We cast the post-tuning into a binary quadratic optimization framework and, despite its NP-hardness, give a practical algorithm to efficiently obtain a high-quality solution. Experimental results on five noted image benchmarks show that our PTH improves previous state-of-the-art methods by 13-58% in mean average precision.
{"title":"Post Tuned Hashing: A New Approach to Indexing High-dimensional Data","authors":"Zhendong Mao, Quan Wang, Yongdong Zhang, Bin Wang","doi":"10.1145/3240508.3240529","DOIUrl":"https://doi.org/10.1145/3240508.3240529","url":null,"abstract":"Learning to hash has proven to be an effective solution for indexing high-dimensional data by projecting them to similarity-preserving binary codes. However, most existing methods end up the learning scheme with a binarization stage, i.e. binary quantization, which inevitably destroys the neighborhood structure of original data. As a result, those methods still suffer from great similarity loss and result in unsatisfactory indexing performance. In this paper we propose a novel hashing model, namely Post Tuned Hashing (PTH), which includes a new post-tuning stage to refine the binary codes after binarization. The post-tuning seeks to rebuild the destroyed neighborhood structure, and hence significantly improves the indexing performance. We cast the post-tuning into a binary quadratic optimization framework and, despite its NP-hardness, give a practical algorithm to efficiently obtain a high-quality solution. Experimental results on five noted image benchmarks show that our PTH improves previous state-of-the-art methods by 13-58% in mean average precision.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"2255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130225687","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}
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