Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval最新文献_第7页

Multimodal Continuous Prediction of Emotions in Movies using Long Short-Term Memory Networks 利用长短期记忆网络对电影中的情绪进行多模态连续预测

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206076

S. Sivaprasad, Tanmayee Joshi, Rishabh Agrawal, N. Pedanekar

引用次数: 17

Facial Expression Synthesis by U-Net Conditional Generative Adversarial Networks 基于U-Net条件生成对抗网络的面部表情合成

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206068

Xueping Wang, Weixin Li, Guodong Mu, Di Huang, Yunhong Wang

引用次数: 24

Ranking News-Quality Multimedia 排名新闻质量多媒体

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206053

G. Marcelino, Ricardo Pinto, João Magalhães

引用次数: 6

Automated Scanning and Individual Identification System for Parts without Marking or Tagging 无标记或标签零件的自动扫描和单独识别系统

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206088

Kengo Makino, W. Duan, Rui Ishiyama, Toru Takahashi, Yuta Kudo, P. Jonker

引用次数: 0

Promoting Open Innovations in Real Estate Tech: Provision of the LIFULL HOME'S Data Set and Collaborative Studies 促进房地产技术的开放式创新:提供liffull HOME的数据集和合作研究

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3210494

Yoji Kiyota

引用次数: 2

Instance Image Retrieval by Aggregating Sample-based Discriminative Characteristics 基于样本判别特征聚合的实例图像检索

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206069

Zhongyang Zhang, Lei Wang, Yang Wang, Luping Zhou, Jianjia Zhang, Fangxiao Chen

{"title":"Instance Image Retrieval by Aggregating Sample-based Discriminative Characteristics","authors":"Zhongyang Zhang, Lei Wang, Yang Wang, Luping Zhou, Jianjia Zhang, Fangxiao Chen","doi":"10.1145/3206025.3206069","DOIUrl":"https://doi.org/10.1145/3206025.3206069","url":null,"abstract":"Identifying the discriminative characteristic of a query is important for image retrieval. For retrieval without human interaction, such characteristic is usually obtained by average query expansion (AQE) or its discriminative variant (DQE) learned from pseudo-examples online, among others. In this paper, we propose a new query expansion method to further improve the above ones. The key idea is to learn a \"unique'' discriminative characteristic for each database image, in an offline manner. During retrieval, the characteristic of a query is obtained by aggregating the unique characteristics of the query-relevant images collected from an initial retrieval result. Compared with AQE which works in the original feature space, our method works in the space of the unique characteristics of database images, significantly enhancing the discriminative power of the characteristic identified for a query. Compared with DQE, our method needs neither pseudo-labeled negatives nor the online learning process, leading to more efficient retrieval and even better performance. The experimental study conducted on seven benchmark datasets verifies the considerable improvement achieved by the proposed method, and also demonstrates its application to the state-of-the-art diffusion-based image retrieval.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"86 26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126140667","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

Annotating, Understanding, and Predicting Long-term Video Memorability 注释、理解和预测长期视频记忆

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206056

Romain Cohendet, Karthik Yadati, Ngoc Q. K. Duong, C. Demarty

{"title":"Annotating, Understanding, and Predicting Long-term Video Memorability","authors":"Romain Cohendet, Karthik Yadati, Ngoc Q. K. Duong, C. Demarty","doi":"10.1145/3206025.3206056","DOIUrl":"https://doi.org/10.1145/3206025.3206056","url":null,"abstract":"Memorability can be regarded as a useful metric of video importance to help make a choice between competing videos. Research on computational understanding of video memorability is however in its early stages. There is no available dataset for modelling purposes, and the few previous attempts provided protocols to collect video memorability data that would be difficult to generalize. Furthermore, the computational features needed to build a robust memorability predictor remain largely undiscovered. In this article, we propose a new protocol to collect long-term video memorability annotations. We measure the memory performances of 104 participants from weeks to years after memorization to build a dataset of 660 videos for video memorability prediction. This dataset is made available for the research community. We then analyze the collected data in order to better understand video memorability, in particular the effects of response time, duration of memory retention and repetition of visualization on video memorability. We finally investigate the use of various types of audio and visual features and build a computational model for video memorability prediction. We conclude that high level visual semantics help better predict the memorability of videos.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122482958","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: 32

Learning Perceptual Embeddings with Two Related Tasks for Joint Predictions of Media Interestingness and Emotions 用两个相关任务学习感知嵌入对媒体兴趣和情绪的联合预测

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3206071

Yang Liu, Zhonglei Gu, Tobey H. Ko, K. Hua

{"title":"Learning Perceptual Embeddings with Two Related Tasks for Joint Predictions of Media Interestingness and Emotions","authors":"Yang Liu, Zhonglei Gu, Tobey H. Ko, K. Hua","doi":"10.1145/3206025.3206071","DOIUrl":"https://doi.org/10.1145/3206025.3206071","url":null,"abstract":"Integrating media elements of various medium, multimedia is capable of expressing complex information in a neat and compact way. Early studies have linked different sensory presentation in multimedia with the perception of human-like concepts. Yet, the richness of information in multimedia makes understanding and predicting user perceptions in multimedia content a challenging task both to the machine and the human mind. This paper presents a novel multi-task feature extraction method for accurate prediction of user perceptions in multimedia content. Differentiating from the conventional feature extraction algorithms which focus on perfecting a single task, the proposed model recognizes the commonality between different perceptions (e.g., interestingness and emotional impact), and attempts to jointly optimize the performance of all the tasks through uncovered commonality features. Using both a media interestingness dataset and a media emotion dataset for user perception prediction tasks, the proposed model attempts to simultaneously characterize the individualities of each task and capture the commonalities shared by both tasks, and achieves better accuracy in predictions than other competing algorithms on real-world datasets of two related tasks: MediaEval 2017 Predicting Media Interestingness Task and MediaEval 2017 Emotional Impact of Movies Task.","PeriodicalId":224132,"journal":{"name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124165120","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: 4

Recommendation Technologies for Multimedia Content 多媒体内容推荐技术

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3206025.3210497

Xiangnan He, Hanwang Zhang, Tat-Seng Chua

引用次数: 0

Session details: Oral Session 4: Video Analysis 会议详情:口头会议4:视频分析

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval Pub Date : 2018-06-05 DOI: 10.1145/3252929

K. Shinoda

引用次数: 0