首页/论文/1805.01222

arXiv 1805.012222018-05-03cs.CVcs.LGcs.SDeess.ASstat.ML

audEERING's approach to the One-Minute-Gradual Emotion Challenge

audEERING GmbH（audEERING有限公司）

Andreas Triantafyllopoulos, Hesam Sagha, Florian Eyben, Björn Schuller

备注：: 3 pages

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficient (CCC) for arousal (from audio) and .401 for valence (from video).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge

Ping An, Gamma Lab（平安伽马实验室）

The continuous dimensional emotion modelled by arousal and valence can depict complex changes of emotions. In this paper, we present our works on arousal and valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal representations are first extracted from videos using a variety of acoustic, video and textual models and support vector machine (SVM) is then used for fusion of multimodal signals to make final predictions. Our solution achieves Concordant Correlation Coefficient (CCC) scores of 0.397 and 0.520 on arousal and valence respectively for the validation dataset, wh…

2018-05-03cs.AIcs.CLcs.CV

Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs

Jacobs University Bremen（不来梅雅各布大学）

In this paper, we describe our approach for the OMG- Emotion Challenge 2018. The goal is to produce utterance-level valence and arousal estimations for videos of approximately 1 minute length. We tackle this problem by first extracting facial expressions features of videos as time series data, and then using Recurrent Neural Networks of the Echo State Network type to model the correspondence between the time series data and valence-arousal values. Experimentally we show that the proposed approach surpasses the baseline methods provided by the organizers.

2018-05-02cs.CV

OMG - Emotion Challenge Solution

Huazhong University of Science and Technology（华中科技大学）

This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then used a spectral approach to fuse them to obtain improved performance.

2018-04-30cs.CVcs.LGcs.SD

A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues

ADSC-UIUC（ADSC-伊利诺伊大学厄巴纳-香槟分校）；INRIA Grenoble（法国国家信息与自动化研究所格勒诺布尔分院）；Tencent AI Lab（腾讯人工智能实验室）

In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.

2019-06-25cs.CV

Dimensional emotion recognition using visual and textual cues

Faculty of Engineering of University of Porto (FEUP)（波尔图大学工程学院）；INESC TEC（波尔图工程学院科技与高等研究中心）；Faculty of Science of University of Porto (FCUP)（波尔图大学理学院）；UCP-ESB（葡萄牙天主教大学商业经济学院）

This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge). The underlying objective of the challenge is the automatic estimation of emotion expressions in the two-dimensional emotion representation space (i.e., arousal and valence). The adopted methodology is a weighted ensemble of several models from both video and text modalities. For video-based recognition, two different types of visual cues (i.e., face and facial landmarks) were considered to feed a multi-input deep neural network. Regardin…

2018-05-03cs.AIcs.CLcs.CV

Multimodal Utterance-level Affect Analysis using Visual, Audio and Text Features

Hong Kong University of Science and Technology（香港科技大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）

The integration of information across multiple modalities and across time is a promising way to enhance the emotion recognition performance of affective systems. Much previous work has focused on instantaneous emotion recognition. The 2018 One-Minute Gradual-Emotion Recognition (OMG-Emotion) challenge, which was held in conjunction with the IEEE World Congress on Computational Intelligence, encouraged participants to address long-term emotion recognition by integrating cues from multiple modalities, including facial expression, audio and language. Intuitively, a multi-modal inference network s…

2018-05-04cs.CLcs.CVeess.IV

更多相关论文收起更多论文

二合一：基于解耦主体-上下文 Transformer 的单阶段情感识别

Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer

College of Big Data and Internet, Shenzhen Technology University (SZTU)（深圳技术大学大数据与互联网学院）；The University of Hong Kong (HKU)（香港大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU)（西北工业大学人工智能与光学电子学院）；School of Engineering, Southern University of Science and Technology (SUSTech)（南方科技大学工程学院）

针对情感识别两阶段流程训练脱节和交互受限的问题，提出单阶段 Decoupled Subject-Context Transformer (DSCT)，通过联合监督和解耦后融合机制实现主体定位与情感分类，在 CAER-S 和 EMOTIC 数据集上以更少参数超越二阶段方法。

2024-04-29cs.CV