# AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge

arXiv: https://arxiv.org/abs/1605.01600 · HTML: https://mlsanity.com/paper/1605.01600/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2016.

## Built on by (most cited first, 30 of 69)

- [MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning](https://mlsanity.com/paper/2304.08981/) (2023)
- [Pre-training strategies and datasets for facial representation learning](https://mlsanity.com/paper/2103.16554/) (2021)
- [Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3](https://mlsanity.com/paper/2203.13031/) (2022)
- [Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition](https://mlsanity.com/paper/2111.05222/) (2021)
- [Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention](https://mlsanity.com/paper/2209.09068/) (2022)
- [Representation learning through cross-modal conditional teacher-student training for speech emotion recognition](https://mlsanity.com/paper/2112.00158/) (2021)
- [Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion](https://mlsanity.com/paper/2107.01175/) (2021)
- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews](https://mlsanity.com/paper/2404.14463/) (2024)
- [DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection](https://mlsanity.com/paper/2409.15936/) (2024)
- [From the Lab to the Wild: Affect Modeling via Privileged Information](https://mlsanity.com/paper/2305.10919/) (2023)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks](https://mlsanity.com/paper/2110.03299/) (2021)
- [Affect Recognition in Conversations Using Large Language Models](https://mlsanity.com/paper/2309.12881/) (2023)
- [Multistage linguistic conditioning of convolutional layers for speech emotion recognition](https://mlsanity.com/paper/2110.06650/) (2021)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [DEPAC: a Corpus for Depression and Anxiety Detection from Speech](https://mlsanity.com/paper/2306.12443/) (2023)
- [LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the Wild](https://mlsanity.com/paper/2407.00024/) (2024)
- [End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning](https://mlsanity.com/paper/2209.15449/) (2022)
- [Scaling Representation Learning from Ubiquitous ECG with State-Space Models](https://mlsanity.com/paper/2309.15292/) (2023)
- [Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space](https://mlsanity.com/paper/2503.10104/) (2025)
- [The First MPDD Challenge: Multimodal Personality-aware Depression Detection](https://mlsanity.com/paper/2505.10034/) (2025)
- [Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding](https://mlsanity.com/paper/2510.04899/) (2025)
- [Automatic Depression Detection via Learning and Fusing Features from Visual Cues](https://mlsanity.com/paper/2203.00304/) (2022)
- [A knowledge-driven vowel-based approach of depression classification from speech using data augmentation](https://mlsanity.com/paper/2210.15261/) (2022)
- [Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities](https://mlsanity.com/paper/2407.06125/) (2024)
- [Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks](https://mlsanity.com/paper/2203.11111/) (2022)
- [Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions](https://mlsanity.com/paper/2207.12135/) (2022)
- [Use of Affective Visual Information for Summarization of Human-Centric Videos](https://mlsanity.com/paper/2107.03783/) (2021)
- [Toward Knowledge-Driven Speech-Based Models of Depression: Leveraging Spectrotemporal Variations in Speech Vowels](https://mlsanity.com/paper/2210.02527/) (2022)

## Abstract (arXiv)

The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.