- General Chairs:
- Fabien Ringeval,
- Björn Schuller,
- Michel Valstar,
- Jonathan Gratch,
- Roddy Cowie,
- Maja Pantic
- Sponsor:
- sigmm
It is our great pleasure to welcome you to the 7th Audio-Visual Emotion Challenge -- AVEC'17, held in conjunction with the ACM Multimedia 2017 in Mountain View, CA, USA.
This year's challenge and associated workshop continues to push the boundaries of audio-visual emotion and depression recognition towards real-life applications of behavioural computing. Looking back in the history of AVEC, the first challenge posed the problem of detecting discrete emotion classes on a large set of natural behaviour data. The second AVEC extended this problem to the prediction of continuous valued dimensional affect. This problem was enlarged further for the third edition to include the prediction of self-reported severity of depression. The fourth edition was a refined run with enriched annotations. The fifth AVEC introduced physiological signals, along with audio-visual data, for the prediction of dimensional affect. In the sixth edition, we introduced human-agent interactions for depression analysis, in addition to affect recognition. Finally, this year we've focused the study of affect from human behaviours captured 'in-the-wild', along with depression analysis from human-agent interactions.
The mission of AVEC challenge and workshop series is to provide a common benchmark test set for individual multimodal information processing and to bring together the audio, video and audio-visual emotion recognition communities, to compare the relative merits of the approaches to emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. The main underlying motivation is the need to advance emotion recognition and depression estimation for multimedia retrieval to a level where behaviours can be reliably sensed in real-life conditions, as this is exactly the type of data that applications would have to face in the real world.
Proceeding Downloads
Body Language Without a Body: Nonverbal Communication in Technology Mediated Settings
Humans are wired for face-to-face interaction because this was the only possible and available setting during the long evolutionary process that has led to Homo Sapiens. At the moment an increasingly significant fraction of our interactions take place ...
AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge
- Fabien Ringeval,
- Björn Schuller,
- Michel Valstar,
- Jonathan Gratch,
- Roddy Cowie,
- Stefan Scherer,
- Sharon Mozgai,
- Nicholas Cummins,
- Maximilian Schmitt,
- Maja Pantic
The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) "Real-life depression, and affect" will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and ...
Continuous Multimodal Emotion Prediction Based on Long Short Term Memory Recurrent Neural Network
The continuous dimensional emotion can depict subtlety and complexity of emotional change, which is an inherently challenging problem with growing attention. This paper presents our automatic prediction of dimensional emotional state for Audio-Visual ...
Multimodal Multi-task Learning for Dimensional and Continuous Emotion Recognition
Automatic emotion recognition is a challenging task which can make great impact on improving natural human computer interactions. In this paper, we present our effort for the Affect Subtask in the Audio/Visual Emotion Challenge (AVEC) 2017, which ...
Investigating Word Affect Features and Fusion of Probabilistic Predictions Incorporating Uncertainty in AVEC 2017
- Ting Dang,
- Brian Stasak,
- Zhaocheng Huang,
- Sadari Jayawardena,
- Mia Atcheson,
- Munawar Hayat,
- Phu Le,
- Vidhyasaharan Sethu,
- Roland Goecke,
- Julien Epps
Predicting emotion intensity and severity of depression are both challenging and important problems within the broader field of affective computing. As part of the AVEC 2017, we developed a number of systems to accomplish these tasks. In particular, ...
Depression Severity Prediction Based on Biomarkers of Psychomotor Retardation
This paper addresses the AVEC 2017 ? Depression Sub-Challenge, where the objective is to propose methods which can aid automated prediction of depression severity. In this paper, we specifically focus on biomarkers of psychomotor retardation, which are ...
Hybrid Depression Classification and Estimation from Audio Video and Text Information
In this paper, we design a hybrid depression classification and depression estimation framework from audio, video and text descriptors. It contains three main components: 1) Deep Convolutional Neural Network (DCNN) and Deep Neural Network (DNN) based ...
Multimodal Measurement of Depression Using Deep Learning Models
This paper addresses multi-modal depression analysis. We propose a multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models. Our framework considers audio, video and text streams. For each ...
A Random Forest Regression Method With Selected-Text Feature For Depression Assessment
Audio/visual and mood disorder cues have been recently explored to assist psychologists and psychiatrists in Depression Diagnosis. In this paper, we propose a random forest method with a Selected-Text feature which is according to the analysis on the ...
Topic Modeling Based Multi-modal Depression Detection
Major depressive disorder is a common mental disorder that affects almost 7% of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC) asks participants to build a model to predict depression levels based on the audio, video, and text ...
Cited By
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Sun G, Zhao S, Zou B, An Y, Lu Y and Cheng C (2022). Multimodal depression detection using a deep feature fusion network International Conference on Computer Science and Communication Technology (ICCSCT 2022), 10.1117/12.2662620, 9781510661240, (269)
- Yin S, Liang C, Ding H and Wang S A Multi-Modal Hierarchical Recurrent Neural Network for Depression Detection Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop, (65-71)
Index Terms
- Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge