Proceedings of the 2020 International Conference on Multimodal Interaction最新文献_第10页

Leniency to those who confess?: Predicting the Legal Judgement via Multi-Modal Analysis 对认罪的人从宽处理?:用多模态分析预测法律判决

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418893

Liang Yang, Jingjie Zeng, Tao Peng, Xi Luo, Jinghui Zhang, Hongfei Lin

引用次数: 2

Gaze Tracker Accuracy and Precision Measurements in Virtual Reality Headsets 虚拟现实头戴式耳机中注视跟踪器的精度和精度测量

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418816

J. Kangas, Olli Koskinen, R. Raisamo

引用次数: 1

Punchline Detection using Context-Aware Hierarchical Multimodal Fusion 使用上下文感知分层多模态融合的笑点检测

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418891

Akshat Choube, M. Soleymani

引用次数: 8

Analyzing Nonverbal Behaviors along with Praising 在赞美的同时分析非语言行为

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418868

Toshiki Onishi, Arisa Yamauchi, Ryo Ishii, Y. Aono, Akihiro Miyata

引用次数: 5

Zero-Shot Learning for Gesture Recognition 零射击学习手势识别

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3421161

Naveen Madapana

{"title":"Zero-Shot Learning for Gesture Recognition","authors":"Naveen Madapana","doi":"10.1145/3382507.3421161","DOIUrl":"https://doi.org/10.1145/3382507.3421161","url":null,"abstract":"Zero-Shot Learning (ZSL) is a new paradigm in machine learning that aims to recognize the classes that are not present in the training data. Hence, this paradigm is capable of comprehending the categories that were never seen before. While deep learning has pushed the limits of unseen object recognition, ZSL for temporal problems such as unfamiliar gesture recognition (referred to as ZSGL) remain unexplored. ZSGL has the potential to result in efficient human-machine interfaces that can recognize and understand the spontaneous and conversational gestures of humans. In this regard, the objective of this work is to conceptualize, model and develop a framework to tackle ZSGL problems. The first step in the pipeline is to develop a database of gesture attributes that are representative of a range of categories. Next, a deep architecture consisting of convolutional and recurrent layers is proposed to jointly optimize the semantic and classification losses. Lastly, rigorous experiments are performed to compare the proposed model with respect to existing ZSL models on CGD 2013 and MSRC-12 datasets. In our preliminary work, we identified a list of 64 discriminative attributes related to gestures' morphological characteristics. Our approach yields an unseen class accuracy of (41%) which outperforms the state-of-the-art approaches by a considerable margin. Future work involves the following: 1. Modifying the existing architecture in order to improve the ZSL accuracy, 2. Augmenting the database of attributes to incorporate semantic properties, 3. Addressing the issue of data imbalance which is inherent to ZSL problems, and 4. Expanding this research to other domains such as surgeme and action recognition.","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121710632","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: 5

Facilitating Flexible Force Feedback Design with Feelix 利用Feelix促进灵活的力反馈设计

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418819

Anke van Oosterhout, M. Bruns, Eve E. Hoggan

引用次数: 7

Speaker-Invariant Adversarial Domain Adaptation for Emotion Recognition 情绪识别的说话人不变对抗域自适应

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418813

Yufeng Yin, Baiyu Huang, Yizhen Wu, M. Soleymani

{"title":"Speaker-Invariant Adversarial Domain Adaptation for Emotion Recognition","authors":"Yufeng Yin, Baiyu Huang, Yizhen Wu, M. Soleymani","doi":"10.1145/3382507.3418813","DOIUrl":"https://doi.org/10.1145/3382507.3418813","url":null,"abstract":"Automatic emotion recognition methods are sensitive to the variations across different datasets and their performance drops when evaluated across corpora. We can apply domain adaptation techniques e.g., Domain-Adversarial Neural Network (DANN) to mitigate this problem. Though the DANN can detect and remove the bias between corpora, the bias between speakers still remains which results in reduced performance. In this paper, we propose Speaker-Invariant Domain-Adversarial Neural Network (SIDANN) to reduce both the domain bias and the speaker bias. Specifically, based on the DANN, we add a speaker discriminator to unlearn information representing speakers' individual characteristics with a gradient reversal layer (GRL). Our experiments with multimodal data (speech, vision, and text) and the cross-domain evaluation indicate that the proposed SIDANN outperforms (+5.6% and +2.8% on average for detecting arousal and valence) the DANN model, suggesting that the SIDANN has a better domain adaptation ability than the DANN. Besides, the modality contribution analysis shows that the acoustic features are the most informative for arousal detection while the lexical features perform the best for valence detection.","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396449","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: 19

LieCatcher: Game Framework for Collecting Human Judgments of Deceptive Speech LieCatcher:收集人类对欺骗性言语判断的游戏框架

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3421166

Sarah Ita Levitan, James Shin, Ivy Chen, Julia Hirschberg

引用次数: 4

Group-level Speech Emotion Recognition Utilising Deep Spectrum Features 基于深度频谱特征的群体级语音情感识别

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3417964

Sandra Ottl, S. Amiriparian, Maurice Gerczuk, Vincent Karas, Björn Schuller

引用次数: 22

Understanding Applicants' Reactions to Asynchronous Video Interviews Through Self-reports and Nonverbal Cues 通过自我报告和非语言线索了解应聘者对非同步视频面试的反应

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418869

Skanda Muralidhar, E. Kleinlogel, E. Mayor, Adrian Bangerter, M. S. Mast, D. Gática-Pérez

{"title":"Understanding Applicants' Reactions to Asynchronous Video Interviews Through Self-reports and Nonverbal Cues","authors":"Skanda Muralidhar, E. Kleinlogel, E. Mayor, Adrian Bangerter, M. S. Mast, D. Gática-Pérez","doi":"10.1145/3382507.3418869","DOIUrl":"https://doi.org/10.1145/3382507.3418869","url":null,"abstract":"Asynchronous video interviews (AVIs) are increasingly used by organizations in their hiring process. In this mode of interviewing, the applicants are asked to record their responses to predefined interview questions using a webcam via an online platform. AVIs have increased usage due to employers' perceived benefits in terms of costs and scale. However, little research has been conducted regarding applicants' reactions to these new interview methods. In this work, we investigate applicants' reactions to an AVI platform using self-reported measures previously validated in psychology literature. We also investigate the connections of these measures with nonverbal behavior displayed during the interviews. We find that participants who found the platform creepy and had concerns about privacy reported lower interview performance compared to participants who did not have such concerns. We also observe weak correlations between nonverbal cues displayed and these self-reported measures. Finally, inference experiments achieve overall low-performance w.r.t. to explaining applicants' reactions. Overall, our results reveal that participants who are not at ease with AVIs (i.e., high creepy ambiguity score) might be unfairly penalized. This has implications for improved hiring practices using AVIs.","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131770988","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