# EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

arXiv: https://arxiv.org/abs/1808.07773 · HTML: https://mlsanity.com/paper/1808.07773/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 18 of 18)

- [FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2203.09463/) (2022)
- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning](https://mlsanity.com/paper/2304.08981/) (2023)
- [Adaptive Log-Euclidean Metrics for SPD Matrix Learning](https://mlsanity.com/paper/2303.15477/) (2023)
- [Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition](https://mlsanity.com/paper/2307.11404/) (2023)
- [Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition](https://mlsanity.com/paper/2209.12172/) (2022)
- [Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark](https://mlsanity.com/paper/2109.11243/) (2021)
- [Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning](https://mlsanity.com/paper/2310.04306/) (2023)
- [Multi-modal Residual Perceptron Network for Audio-Video Emotion Recognition](https://mlsanity.com/paper/2107.10742/) (2021)
- [A Survey of Deep Learning for Group-level Emotion Recognition](https://mlsanity.com/paper/2408.15276/) (2024)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)
- [Automatic Sensor-free Affect Detection: A Systematic Literature Review](https://mlsanity.com/paper/2310.13711/) (2023)
- [Hi-EF: Benchmarking Emotion Forecasting in Human-interaction](https://mlsanity.com/paper/2407.16406/) (2024)
- [Gems: Group Emotion Profiling Through Multimodal Situational Understanding](https://mlsanity.com/paper/2507.22393/) (2025)
- [Incorporating Scene Context and Semantic Labels for Enhanced Group-level Emotion Recognition](https://mlsanity.com/paper/2509.21747/) (2025)
- [LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics](https://mlsanity.com/paper/2604.07193/) (2026)
- [GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos](https://mlsanity.com/paper/2604.16214/) (2026)
- [LG-GER: Language-Guided Group Emotion Recognition via Multimodal Evidence Distillation](https://mlsanity.com/paper/2608.23880/) (2026)

## Abstract (arXiv)

This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colorado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on `in the wild' data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.