# FindingEmo: An Image Dataset for Emotion Recognition in the Wild

arXiv: https://arxiv.org/abs/2402.01355 · HTML: https://mlsanity.com/paper/2402.01355/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Context-Aware Emotion Recognition Networks](https://mlsanity.com/paper/1908.05913/) (2019)
- [Context Based Emotion Recognition using EMOTIC Dataset](https://mlsanity.com/paper/2003.13401/) (2020)
- [EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle](https://mlsanity.com/paper/2003.06692/) (2020)
- [Facial Emotion Recognition with Noisy Multi-task Annotations](https://mlsanity.com/paper/2010.09849/) (2020)

## Built on by (most cited first, 22 of 22)

- [MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models](https://mlsanity.com/paper/2408.02718/) (2024)
- [EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model](https://mlsanity.com/paper/2501.05710/) (2025)
- [EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation](https://mlsanity.com/paper/2506.03652/) (2025)
- [EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis](https://mlsanity.com/paper/2511.12554/) (2025)
- [EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition](https://mlsanity.com/paper/2505.20033/) (2025)
- [EmoFeedback2: Reinforcement of Continuous Emotional Image Generation via LVLM-based Reward and Textual Feedback](https://mlsanity.com/paper/2511.19982/) (2025)
- [EEmo-Logic: A Unified Dataset and Multi-Stage Framework for Comprehensive Image-Evoked Emotion Assessment](https://mlsanity.com/paper/2602.01173/) (2026)
- [From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images](https://mlsanity.com/paper/2511.22805/) (2025)
- [Are Vision Language Models Cross-Cultural Theory of Mind Reasoners?](https://mlsanity.com/paper/2512.17394/) (2025)
- [CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation](https://mlsanity.com/paper/2603.09286/) (2026)
- [EmoScene: A Dual-space Dataset for Controllable Affective Image Generation](https://mlsanity.com/paper/2604.00933/) (2026)
- [Enhancing Computer Vision with Knowledge: a Rummikub Case Study](https://mlsanity.com/paper/2411.18172/) (2024)
- [Assessing the Alignment of Popular CNNs to the Brain for Valence Appraisal](https://mlsanity.com/paper/2509.21384/) (2025)
- [Visual Affect Analysis: Predicting Emotions of Image Viewers with Vision-Language Models](https://mlsanity.com/paper/2602.00123/) (2026)
- [GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos](https://mlsanity.com/paper/2604.16214/) (2026)
- [MultiEmo-Bench: Multi-label Visual Emotion Analysis for Multi-modal Large Language Models](https://mlsanity.com/paper/2605.14635/) (2026)
- [Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models](https://mlsanity.com/paper/2605.15755/) (2026)
- [Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis](https://mlsanity.com/paper/2605.26262/) (2026)
- [ArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2608.03358/) (2026)
- [ArtSociety: Multi-Agent Multimodal Collaboration for Art Emotion Understanding](https://mlsanity.com/paper/2609.13240/) (2026)
- [Towards Cost-Efficient Video Dataset Construction with Large Language Models](https://mlsanity.com/paper/2610.16857/) (2026)
- [Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers](https://mlsanity.com/paper/2610.16892/) (2026)

## Abstract (arXiv)

We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition. Contrary to existing datasets, it focuses on complex scenes depicting multiple people in various naturalistic, social settings, with images being annotated as a whole, thereby going beyond the traditional focus on faces or single individuals. Annotated dimensions include Valence, Arousal and Emotion label, with annotations gathered using Prolific. Together with the annotations, we release the list of URLs pointing to the original images, as well as all associated source code.