Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval最新文献_第2页

Cross-Modal Deep Neural Networks based Smartphone Authentication for Intelligent Things System 基于跨模态深度神经网络的智能手机认证

Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval Pub Date : 2021-08-21 DOI: 10.1145/3463944.3469101

Tran Anh Khoa, Dinh Nguyen The Truong, Duc Ngoc Minh Dang

引用次数: 1

Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling Twitter和Facebook上的双面人:收集社交多媒体用于人类个性分析

Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval Pub Date : 2021-06-20 DOI: 10.1145/3463944.3469270

Qi Yang, Aleksandr Farseev, A. Filchenkov

{"title":"Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling","authors":"Qi Yang, Aleksandr Farseev, A. Filchenkov","doi":"10.1145/3463944.3469270","DOIUrl":"https://doi.org/10.1145/3463944.3469270","url":null,"abstract":"Human personality traits are the key drivers behind our decision-making, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called \"PERS\" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122141632","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

ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos ST-HOI:视频中人-物交互检测的时空基线

Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval Pub Date : 2021-05-25 DOI: 10.1145/3463944.3469097

Meng-Jiun Chiou, Chun-Yu Liao, Li-Wei Wang, Roger Zimmermann, Jiashi Feng

{"title":"ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos","authors":"Meng-Jiun Chiou, Chun-Yu Liao, Li-Wei Wang, Roger Zimmermann, Jiashi Feng","doi":"10.1145/3463944.3469097","DOIUrl":"https://doi.org/10.1145/3463944.3469097","url":null,"abstract":"Detecting human-object interactions (HOI) is an important step toward a comprehensive visual understanding of machines. While detecting non-temporal HOIs (e.g., sitting on a chair) from static images is feasible, it is unlikely even for humans to guess temporal-related HOIs (e.g., opening/closing a door) from a single video frame, where the neighboring frames play an essential role. However, conventional HOI methods operating on only static images have been used to predict temporal-related interactions, which is essentially guessing without temporal contexts and may lead to sub-optimal performance. In this paper, we bridge this gap by detecting video-based HOIs with explicit temporal information. We first show that a naive temporal-aware variant of a common action detection baseline does not work on video-based HOIs due to a feature-inconsistency issue. We then propose a simple yet effective architecture named Spatial-Temporal HOI Detection (ST-HOI) utilizing temporal information such as human and object trajectories, correctly-localized visual features, and spatial-temporal masking pose features. We construct a new video HOI benchmark dubbed VidHOI where our proposed approach serves as a solid baseline.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129353848","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

Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement 基于散射变换的正交补投影图像聚类

Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval Pub Date : 2020-11-23 DOI: 10.1145/3463944.3469098

Angel Villar-Corrales, V. Morgenshtern

{"title":"Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement","authors":"Angel Villar-Corrales, V. Morgenshtern","doi":"10.1145/3463944.3469098","DOIUrl":"https://doi.org/10.1145/3463944.3469098","url":null,"abstract":"In the last few years, large improvements in image clustering have been driven by the recent advances in deep learning. However, due to the architectural complexity of deep neural networks, there is no mathematical theory that explains the success of deep clustering techniques. In this work we introduce Projected-Scattering Spectral Clustering (PSSC), a state-of-the-art, stable, and fast algorithm for image clustering, which is also mathematically interpretable. PSSC includes a novel method to exploit the geometric structure of the scattering transform of small images. This method is inspired by the observation that, in the scattering transform domain, the subspaces formed by the eigenvectors corresponding to the few largest eigenvalues of the data matrices of individual classes are nearly shared among different classes. Therefore, projecting out those shared subspaces reduces the intra-class variability, substantially increasing the clustering performance. We call this method 'Projection onto Orthogonal Complement' (POC). Our experiments demonstrate that PSSC obtains the best results among all shallow clustering algorithms. Moreover, it achieves comparable clustering performance to that of recent state-of-the-art clustering techniques, while reducing the execution time by more than one order of magnitude.","PeriodicalId":394510,"journal":{"name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116526808","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