# The OMG-Emotion Behavior Dataset

arXiv: https://arxiv.org/abs/1803.05434 · HTML: https://mlsanity.com/paper/1803.05434/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 25 of 25)

- [Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities](https://mlsanity.com/paper/2402.01831/) (2024)
- [Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework](https://mlsanity.com/paper/2103.15792/) (2021)
- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities](https://mlsanity.com/paper/2503.03983/) (2025)
- [Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2208.10335/) (2022)
- [Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality](https://mlsanity.com/paper/2103.06541/) (2021)
- [An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild](https://mlsanity.com/paper/2107.03465/) (2021)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Technical Report for Valence-Arousal Estimation on Affwild2 Dataset](https://mlsanity.com/paper/2105.01502/) (2021)
- [Learning from Label Relationships in Human Affect](https://mlsanity.com/paper/2207.05577/) (2022)
- [Disentangling Prosody Representations with Unsupervised Speech Reconstruction](https://mlsanity.com/paper/2212.06972/) (2022)
- [Audio Dialogues: Dialogues dataset for audio and music understanding](https://mlsanity.com/paper/2404.07616/) (2024)
- [Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox](https://mlsanity.com/paper/2605.27772/) (2026)
- [Technical Report for Valence-Arousal Estimation in ABAW2 Challenge](https://mlsanity.com/paper/2107.03891/) (2021)
- [HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence](https://mlsanity.com/paper/2206.07593/) (2022)
- [MUSER: MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task](https://mlsanity.com/paper/2105.08146/) (2021)
- [Gaze-enhanced Crossmodal Embeddings for Emotion Recognition](https://mlsanity.com/paper/2205.00129/) (2022)
- [CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition](https://mlsanity.com/paper/2208.07221/) (2022)
- [JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama models](https://mlsanity.com/paper/2403.04798/) (2024)
- [Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline](https://mlsanity.com/paper/2407.06115/) (2024)
- [An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild](https://mlsanity.com/paper/2102.09150/) (2021)
- [Analyzing the Influence of Dataset Composition for Emotion Recognition](https://mlsanity.com/paper/2103.03700/) (2021)
- [Benchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset](https://mlsanity.com/paper/2607.10238/) (2026)
- [INTERSPEECH 2009 Emotion Challenge Revisited: Benchmarking 15 Years of Progress in Speech Emotion Recognition](https://mlsanity.com/paper/2406.06401/) (2024)
- [AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow](https://mlsanity.com/paper/2607.13250/) (2026)

## Abstract (arXiv)

This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.