Proceedings of the ACM Multimedia Asia最新文献_第6页

Food Photo Enhancer of One Sample Generative Adversarial Network 单样本生成对抗网络的食物照片增强器

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366605

Shudan Wang, Liang Sun, Weiming Dong, Yong Zhang

引用次数: 0

Deep Feature Interaction Embedding for Pair Matching Prediction 基于深度特征交互嵌入的配对预测

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366597

Luwei Zhang, Xueting Wang, T. Yamasaki

引用次数: 1

Salient Time Slice Pruning and Boosting for Person-Scene Instance Search in TV Series 基于显著时间片的电视剧人-场景实例搜索

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366594

Z. Wang, Fan Yang, S. Satoh

{"title":"Salient Time Slice Pruning and Boosting for Person-Scene Instance Search in TV Series","authors":"Z. Wang, Fan Yang, S. Satoh","doi":"10.1145/3338533.3366594","DOIUrl":"https://doi.org/10.1145/3338533.3366594","url":null,"abstract":"It is common that TV audiences want to quickly browse scenes with certain actors in TV series. Since 2016, the TREC Video Retrieval Evaluation (TRECVID) Instance Search (INS) task has started to focus on identifying a target person in a target scene simultaneously. In this paper, we name this kind of task as P-S INS (Person-Scene Instance Search). To find out P-S instances, most approaches search person and scene separately, and then directly combine the results together by addition or multiplication. However, we find that person and scene INS modules are not always effective at the same time, or they may suppress each other in some situations. Aggregating the results shot after shot is not a good choice. Luckily, for the TV series, video shots are arranged in chronological order. We extend our focus from time point (single video shot) to time slice (multiple consecutive video shots) in the time-line. Through detecting salient time slices, we prune the data. Through evaluating the importance of salient time slices, we boost the aggregation results. Extensive experiments on the large-scale TRECVID INS dataset demonstrate the effectiveness of the proposed method.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116111026","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

Fast and Accurately Measuring Crack Width via Cascade Principal Component Analysis 利用级联主成分分析法快速准确地测量裂缝宽度

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366578

Lijuan Duan, Huiling Geng, Jun Zeng, Junbiao Pang, Qingming Huang

引用次数: 2

Session details: Best Paper Session 会议细节:最佳论文会议

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3379189

Wen-Huang Cheng

引用次数: 0

Shifted Spatial-Spectral Convolution for Deep Neural Networks 深度神经网络的移位空间-频谱卷积

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366575

Yuhao Xu, Hideki Nakayama

{"title":"Shifted Spatial-Spectral Convolution for Deep Neural Networks","authors":"Yuhao Xu, Hideki Nakayama","doi":"10.1145/3338533.3366575","DOIUrl":"https://doi.org/10.1145/3338533.3366575","url":null,"abstract":"Deep convolutional neural networks (CNNs) extract local features and learn spatial representations via convolutions in the spatial domain. Beyond the spatial information, some works also manage to capture the spectral information in the frequency domain by domain switching methods like discrete Fourier transform (DFT) and discrete cosine transform (DCT). However, most works only pay attention to a single domain, which is prone to ignoring other important features. In this work, we propose a novel network structure to combine spatial and spectral convolutions, and extract features in both spatial and frequency domains. The input channels are divided into two groups for spatial and spectral representations respectively, and then integrated for feature fusion. Meanwhile, we design a channel-shifting mechanism to ensure both spatial and spectral information of every channel are equally and adequately obtained throughout the deep networks. Experimental results demonstrate that compared with state-of-the-art CNN models in a single domain, our shifted spatial-spectral convolution based networks achieve better performance on image classification datasets including CIFAR10, CIFAR100 and SVHN, with considerably fewer parameters.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131335939","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

Residual Graph Convolutional Networks for Zero-Shot Learning 零学习残差图卷积网络

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366552

Jiwei Wei, Yang Yang, Jingjing Li, Lei Zhu, Lin Zuo, Heng Tao Shen

{"title":"Residual Graph Convolutional Networks for Zero-Shot Learning","authors":"Jiwei Wei, Yang Yang, Jingjing Li, Lei Zhu, Lin Zuo, Heng Tao Shen","doi":"10.1145/3338533.3366552","DOIUrl":"https://doi.org/10.1145/3338533.3366552","url":null,"abstract":"Most existing Zero-Shot Learning (ZSL) approaches adopt the semantic space as a bridge to classify unseen categories. However, it is difficult to transfer knowledge from seen categories to unseen categories through semantic space, since the correlations among categories are uncertain and ambiguous in the semantic space. In this paper, we formulated zero-shot learning as a classifier weight regression problem. Specifically, we propose a novel Residual Graph Convolution Network (ResGCN) which takes word embeddings and knowledge graph as inputs and outputs a visual classifier for each category. ResGCN can effectively alleviate the problem of over-smoothing and over-fitting. During the test, an unseen image can be classified by ranking the inner product of its visual feature and predictive visual classifiers. Moreover, we provide a new method to build a better knowledge graph. Our approach not only further enhances the correlations among categories, but also makes it easy to add new categories to the knowledge graph. Experiments conducted on the large-scale ImageNet 2011 21K dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130653052","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: 13

Session details: Human Analysis in Multimedia 会议细节:多媒体中的人类分析

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3379193

Bingkun Bao

引用次数: 0

Multi-source User Attribute Inference based on Hierarchical Auto-encoder 基于分层自编码器的多源用户属性推断

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366599

Boyu Zhang, Xiangguo Ding, Xiaowen Huang, Yang Cao, J. Sang, Jian Yu

{"title":"Multi-source User Attribute Inference based on Hierarchical Auto-encoder","authors":"Boyu Zhang, Xiangguo Ding, Xiaowen Huang, Yang Cao, J. Sang, Jian Yu","doi":"10.1145/3338533.3366599","DOIUrl":"https://doi.org/10.1145/3338533.3366599","url":null,"abstract":"With the rapid development of Online Social Networks (OSNs), it is crucial to construct users' portraits from their dynamic behaviors to address the increasing needs for customized information services. Previous work on user attribute inference mainly concentrated on developing advanced features/models or exploiting external information and knowledge but ignored the contradiction between dynamic behaviors and stable demographic attributes, which results in deviation of user understanding To address the contradiction and accurately infer the user attributes, we propose a Multi-source User Attribute Inference algorithm based on Hierarchical Auto-encoder (MUAI-HAE). The basic idea is that: the shared patterns among the same individual's behaviors on different OSNs well indicate his/her stable demographic attributes. The hierarchical autoencoder is introduced to realize this idea by discovering the underlying non-linear correlation between different OSNs. The unsupervised scheme in shared pattern learning alleviates the requirements for the cross-OSN user account and improves the practicability. Off-the-shelf classification methods are then utilized to infer user attributes from the derived shared behavior patterns. The experiments on the real-world datasets from three OSNs demonstrate the effectiveness of the proposed method.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059206","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

Excluding the Misleading Relatedness Between Attributes in Multi-Task Attribute Recognition Network 多任务属性识别网络中属性间误导性关联的排除

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366555

Sirui Cai, Yuchun Fang

引用次数: 0