Proceedings of the 26th ACM international conference on Multimedia最新文献_第10页

A Unified Framework for Multimodal Domain Adaptation 多模态域自适应的统一框架

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3240633

Fan Qi, Xiaoshan Yang, Changsheng Xu

引用次数: 42

Session details: FF-6 会话详情:FF-6

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3286943

B. Huet

引用次数: 0

End2End Semantic Segmentation for 3D Indoor Scenes 三维室内场景的End2End语义分割

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3243933

Na Zhao

引用次数: 7

SoMin.ai: Social Multimedia Influencer Discovery Marketplace SoMin。ai:社交多媒体影响者发现市场

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3241387

Aleksandr Farseev, Kirill Lepikhin, H. Schwartz, Eu Khoon Ang, K. Powar

引用次数: 18

Facial Expression Recognition Enhanced by Thermal Images through Adversarial Learning 基于对抗学习的热图像面部表情识别

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3240608

Bowen Pan, Shangfei Wang

{"title":"Facial Expression Recognition Enhanced by Thermal Images through Adversarial Learning","authors":"Bowen Pan, Shangfei Wang","doi":"10.1145/3240508.3240608","DOIUrl":"https://doi.org/10.1145/3240508.3240608","url":null,"abstract":"Currently, fusing visible and thermal images for facial expression recognition requires two modalities during both training and testing. Visible cameras are commonly used in real-life applications, and thermal cameras are typically only available in lab situations due to their high price. Thermal imaging for facial expression recognition is not frequently used in real-world situations. To address this, we propose a novel thermally enhanced facial expression recognition method which uses thermal images as privileged information to construct better visible feature representation and improved classifiers by incorporating adversarial learning and similarity constraints during training. Specifically, we train two deep neural networks from visible images and thermal images. We impose adversarial loss to enforce statistical similarity between the learned representations of two modalities, and a similarity constraint to regulate the mapping functions from visible and thermal representation to expressions. Thus, thermal images are leveraged to simultaneously improve visible feature representation and classification during training. To mimic real-world scenarios, only visible images are available during testing. We further extend the proposed expression recognition method for partially unpaired data to explore thermal images' supplementary role in visible facial expression recognition when visible images and thermal images are not synchronously recorded. Experimental results on the MAHNOB Laughter database demonstrate that our proposed method can effectively regularize visible representation and expression classifiers with the help of thermal images, achieving state-of-the-art recognition performance.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"35 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":"132903195","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: 3

FoV-Aware Edge Caching for Adaptive 360° Video Streaming 自适应360°视频流的视场感知边缘缓存

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3240680

A. Mahzari, A. T. Nasrabadi, Aliehsan Samiei, R. Prakash

{"title":"FoV-Aware Edge Caching for Adaptive 360° Video Streaming","authors":"A. Mahzari, A. T. Nasrabadi, Aliehsan Samiei, R. Prakash","doi":"10.1145/3240508.3240680","DOIUrl":"https://doi.org/10.1145/3240508.3240680","url":null,"abstract":"In recent years, there has been growing popularity of Virtual Reality (VR), enabled by technologies like 360° video streaming. Streaming 360° video is extremely challenging due to high bandwidth and low latency requirements. Some VR solutions employ adaptive 360° video streaming which tries to reduce bandwidth consumption by only streaming high resolution video for user's Field of View (FoV). FoV is the part of the video which is being viewed by the user at any given time. Although FoV-adaptive 360° video streaming has been helpful in reducing bandwidth requirements, streaming 360° video from distant content servers is still challenging due to network latency. Caching popular content close to the end users not only decreases network latency, but also alleviates network bandwidth demands by reducing the number of future requests that have to be sent all the way to remote content servers. In this paper, we propose a novel caching policy based on users' FoV, called FoV-aware caching policy. In FoV-aware caching policy, we learn a probabilistic model of common-FoV for each 360° video based on previous users' viewing histories to improve caching performance. Through experiments with real users' head movement dataset, we show that our proposed approach improves cache hit ratio compared to Least Frequently Used (LFU) and Least Recently Used (LRU) caching policies by at least 40% and 17%, respectively.","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":"130246256","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: 78

Pseudo Transfer with Marginalized Corrupted Attribute for Zero-shot Learning 带有边缘损坏属性的伪迁移零射击学习

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3240715

Teng Long, Xing Xu, Youyou Li, Fumin Shen, Jingkuan Song, Heng Tao Shen

{"title":"Pseudo Transfer with Marginalized Corrupted Attribute for Zero-shot Learning","authors":"Teng Long, Xing Xu, Youyou Li, Fumin Shen, Jingkuan Song, Heng Tao Shen","doi":"10.1145/3240508.3240715","DOIUrl":"https://doi.org/10.1145/3240508.3240715","url":null,"abstract":"Zero-shot learning (ZSL) aims to recognize unseen classes that are excluded from training classes. ZSL suffers from 1) Zero-shot bias (Z-Bias) --- model is biased towards seen classes because unseen data is inaccessible for training; 2) Zero-shot variance (Z-Variance) --- associating different images to same semantic embedding yields large associating error. To reduce Z-Bias, we propose a pseudo transfer mechanism, where we first synthesize the distribution of unseen data using semantic embeddings, then we minimize the mismatch between the seen distribution and the synthesized unseen distribution. To reduce Z-Variance, we implicitly corrupted one semantic embedding multiple times to generate image-wise semantic vectors, with which our model learn robust classifiers. Lastly, we integrate our Z-Bias and Z-variance reduction techniques with a linear ZSL model to show its usefulness. Our proposed model successfully overcomes the Z-bias and Z-variance problems. Extensive experiments on five benchmark datasets including ImageNet-1K demonstrate that our model outperforms the state-of-the-art methods with fast training.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"249 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":"114158632","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: 35

Session details: Vision-2 (Object & Scene Understanding) 会议细节:Vision-2(对象和场景理解)

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3286922

Zhengjun Zha

引用次数: 0

Robust Correlation Filter Tracking with Shepherded Instance-Aware Proposals 基于引导实例感知提议的鲁棒相关滤波器跟踪

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3240709

Yanjie Liang, Qiangqiang Wu, Yi Liu, Y. Yan, Hanzi Wang

{"title":"Robust Correlation Filter Tracking with Shepherded Instance-Aware Proposals","authors":"Yanjie Liang, Qiangqiang Wu, Yi Liu, Y. Yan, Hanzi Wang","doi":"10.1145/3240508.3240709","DOIUrl":"https://doi.org/10.1145/3240508.3240709","url":null,"abstract":"In recent years, convolutional neural network (CNN) based correlation filter trackers have achieved state-of-the-art results on the benchmark datasets. However, the CNN based correlation filters cannot effectively handle large scale variation and distortion (such as fast motion, background clutter, occlusion, etc.), leading to the sub-optimal performance. In this paper, we propose a novel CNN based correlation filter tracker with shepherded instance-aware proposals, namely DeepCFIAP, which automatically estimates the target scale in each frame and re-detects the target when distortion happens. DeepCFIAP is proposed to take advantage of the merits of both instance-aware proposals and CNN based correlation filters. Compared with the CNN based correlation filter trackers, DeepCFIAP can successfully solve the problems of large scale variation and distortion via the shepherded instance-aware proposals, resulting in more robust tracking performance. Specifically, we develop a novel proposal ranking algorithm based on the similarities between proposals and instances. In contrast to the detection proposal based trackers, DeepCFIAP shepherds the instance-aware proposals towards their optimal positions via the CNN based correlation filters, resulting in more accurate tracking results. Extensive experiments on two challenging benchmark datasets demonstrate that the proposed DeepCFIAP performs favorably against state-of-the-art trackers and it is especially feasible for long-term tracking.","PeriodicalId":339857,"journal":{"name":"Proceedings of the 26th ACM international conference on Multimedia","volume":"50 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":"116485528","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: 9

ChildAR-bot: Educational Playing Projection-based AR Robot for Children ChildAR-bot:儿童教育游戏投影AR机器人

Proceedings of the 26th ACM international conference on Multimedia Pub Date : 2018-10-15 DOI: 10.1145/3240508.3241362

Yoonjung Park, Yoonsik Yang, Hyocheol Ro, Jinwon Cha, Kyuri Kim, T. Han

引用次数: 5