ACM Multimedia Asia最新文献_第5页

FQM-GC: Full-reference Quality Metric for Colored Point Cloud Based on Graph Signal Features and Color Features FQM-GC:基于图信号特征和颜色特征的彩色点云全参考质量度量

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490578

Ke-Xin Zhang, G. Jiang, Mei Yu

引用次数: 2

Automatically Generate Rigged Character from Single Image 自动生成操纵字符从单个图像

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490565

Zhanpeng Huang, Rui Han, Jianwen Huang, Hao Yin, Zipeng Qin, Zibin Wang

引用次数: 0

An Efficient Bus Crowdedness Classification System 一种高效的公交拥挤分类系统

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3493587

Lingcan Meng, Xiushan Nie, Zhifang Tan

引用次数: 0

A Fine-Grained River Ice Semantic Segmentation based on Attentive Features and Enhancing Feature Fusion 基于关注特征和增强特征融合的细粒度河冰语义分割

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3497698

Rui Wang, Chengyu Zheng, Yanru Jiang, Zhao-Hui Wang, Min Ye, Chenglong Wang, Ning Song, Jie Nie

{"title":"A Fine-Grained River Ice Semantic Segmentation based on Attentive Features and Enhancing Feature Fusion","authors":"Rui Wang, Chengyu Zheng, Yanru Jiang, Zhao-Hui Wang, Min Ye, Chenglong Wang, Ning Song, Jie Nie","doi":"10.1145/3469877.3497698","DOIUrl":"https://doi.org/10.1145/3469877.3497698","url":null,"abstract":"The semantic segmentation of frazil ice and anchor ice is of great significance for river management, ship navigation, and ice hazard forecasting in cold regions. Especially, distinguishing frazil ice from sediment-carrying anchor ice can increase the estimation accuracy of the sediment transportation capacity of the river. Although the river ice semantic segmentation methods based on deep learning has achieved great prediction accuracy, there is still the problem of insufficient feature extraction. To address this problem, we proposed a Fine-Grained River Ice Semantic Segmentation (FGRIS) based on attentive features and enhancing feature fusion to deal with these challenges. First, we propose a Dual-Attention Mechanism (DAM) method, which uses a combination of channel attention features and position attention features to extract more comprehensive semantic features. Then, we proposed a novel Branch Feature Fusion (BFF) module to bridge the semantic feature gap between high-level feature semantic features and low-level semantic features, which is robust to different scales. Experimental results conducted on Alberta River Ice Segmentation Dataset demonstrate the superiority of the proposed method.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128081916","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

Impression of a Job Interview training agent that gives rationalized feedback: Should Virtual Agent Give Advice with Rationale? 面试培训代理给出合理反馈的印象:虚拟代理应该给出合理的建议吗?

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3493598

Nao Takeuchi, Tomoko Koda

引用次数: 2

Spherical Image Compression Using Spherical Wavelet Transform 基于球面小波变换的球面图像压缩

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490577

Huan Wang, Yunhui Shi, Jin Wang, Gang Wu, N. Ling, Baocai Yin

{"title":"Spherical Image Compression Using Spherical Wavelet Transform","authors":"Huan Wang, Yunhui Shi, Jin Wang, Gang Wu, N. Ling, Baocai Yin","doi":"10.1145/3469877.3490577","DOIUrl":"https://doi.org/10.1145/3469877.3490577","url":null,"abstract":"The Spherical Measure Based Spherical Image Representation (SMSIR) has nearly uniformly distributed pixels in the spherical domain with effective index schemes. Based on SMSIR, the spherical wavelet transform can be efficiently designed, which can capture the spherical geometry feature in a compact manner and provides a powerful tool for spherical image compression. In this paper, we propose an efficient compression scheme for SMSIR images named Spherical Set Partitioning in Hierarchical Trees (S-SPIHT) using the spherical wavelet transform, which exploits the inherent similarities across the subbands in the spherical wavelet decomposition of a SMSIR image. The proposed S-SPIHT can progressively transform spherical wavelet coefficients into bit-stream, and generate an embedded compressed bit-stream that can be efficiently decoded at several spherical image quality levels. The most crucial part of our proposed S-SPIHT is the redesign of scanning the wavelet coefficients corresponding to different index schemes. We design three scanning methods, namely ordered root tree index scanning (ORTIS), dyadic index progressive scanning(DIPS) and dyadic index cross scanning(DICS)to efficiently reorganize the wavelet coefficients. These methods can effectively exploit the self-similarity between sub-bands and the fact that the high-frequency sub-bands mostly contain insignificant coefficients. Experimental results on widely-used datasets demonstrate that our proposed S-SPIHT outperforms the straightforward SPIHT for SMSIR images in terms of PSNR, S-PSNR and SSIM.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130236949","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

Attention-based Dual-Branches Localization Network for Weakly Supervised Object Localization 基于注意力的弱监督目标双分支定位网络

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490568

Wenjun Hui, Chuangchuang Tan, Guanghua Gu

引用次数: 0

SangeetXML: An XML Format for Score Retrieval for Indic Music 一个用于印度音乐乐谱检索的XML格式

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3493697

Chandan Misra

引用次数: 1

Towards Discriminative Visual Search via Semantically Cycle-consistent Hashing Networks 基于语义循环一致哈希网络的判别视觉搜索

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490583

Zheng Zhang, Jianning Wang, Guangming Lu

{"title":"Towards Discriminative Visual Search via Semantically Cycle-consistent Hashing Networks","authors":"Zheng Zhang, Jianning Wang, Guangming Lu","doi":"10.1145/3469877.3490583","DOIUrl":"https://doi.org/10.1145/3469877.3490583","url":null,"abstract":"Deep hashing has shown great potentials in large-scale visual similarity search due to preferable storage and computation efficiency. Typically, deep hashing encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this area mainly focus on building the relationship between the visual and objective hash space, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic and hashing spaces, leading to serious semantic ignorance problem during space transformation. In this paper, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHN), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a semantic space is conceived to strengthen the flexibility and completeness of categories in feature inference. Moreover, a transitive latent space is formulated to explore the shared semantic interactivity embedded in visual and semantic features. Our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121589772","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: 2

A Reinforcement Learning-Based Reward Mechanism for Molecule Generation that Introduces Activity Information 引入活动信息的基于强化学习的分子生成奖励机制

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3497700

Hao Liu, Jinmeng Yan, Yuandong Zhou

{"title":"A Reinforcement Learning-Based Reward Mechanism for Molecule Generation that Introduces Activity Information","authors":"Hao Liu, Jinmeng Yan, Yuandong Zhou","doi":"10.1145/3469877.3497700","DOIUrl":"https://doi.org/10.1145/3469877.3497700","url":null,"abstract":"In this paper, we propose an activity prediction method for molecule generation based on the framework of reinforcement learning. The method is used as a scoring module for the molecule generation process. By introducing information about known active molecules for specific set of target conformations, it overcomes the traditional molecular optimization strategy where the method only uses computable properties. Eventually, our prediction method improves the quality of the generated molecules. The prediction method utilized fusion features that consist of traditional countable properties of molecules such as atomic number and the binding property of the molecule to the target. Furthermore, this paper designs a ultra large-scale molecular docking parallel computing method, which greatly improves the performance of the molecular docking [1] scoring process. The computing method makes the high-quality docking computing to predict molecular activity possible. The final experimental result shows that the molecule generation model using the prediction method can produce nearly twenty percent active molecules, which shows that the method proposed in this paper can effectively improve the performance of molecule generation.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115944114","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