# EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle

arXiv: https://arxiv.org/abs/2003.06692 · HTML: https://mlsanity.com/paper/2003.06692/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2020.

## Built on by (most cited first, 30 of 50)

- [Context De-confounded Emotion Recognition](https://mlsanity.com/paper/2303.11921/) (2023)
- [Learning Emotion Representations from Verbal and Nonverbal Communication](https://mlsanity.com/paper/2305.13500/) (2023)
- [Robust Emotion Recognition in Context Debiasing](https://mlsanity.com/paper/2403.05963/) (2024)
- [Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition](https://mlsanity.com/paper/2201.05834/) (2022)
- [COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition](https://mlsanity.com/paper/2206.05833/) (2022)
- [FindingEmo: An Image Dataset for Emotion Recognition in the Wild](https://mlsanity.com/paper/2402.01355/) (2024)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Evaluating Vision-Language Models for Emotion Recognition](https://mlsanity.com/paper/2502.05660/) (2025)
- [VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning](https://mlsanity.com/paper/2404.07078/) (2024)
- [The Call for Socially Aware Language Technologies](https://mlsanity.com/paper/2405.02411/) (2024)
- [Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild](https://mlsanity.com/paper/2105.07484/) (2021)
- [An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild](https://mlsanity.com/paper/2107.03465/) (2021)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Large Vision-Language Models as Emotion Recognizers in Context Awareness](https://mlsanity.com/paper/2407.11300/) (2024)
- [Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training](https://mlsanity.com/paper/2407.04963/) (2024)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [Contextual Emotion Recognition using Large Vision Language Models](https://mlsanity.com/paper/2405.08992/) (2024)
- [High-Level Context Representation for Emotion Recognition in Images](https://mlsanity.com/paper/2305.03500/) (2023)
- [Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis](https://mlsanity.com/paper/2207.13064/) (2022)
- [How you feelin'? Learning Emotions and Mental States in Movie Scenes](https://mlsanity.com/paper/2304.05634/) (2023)
- [Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer](https://mlsanity.com/paper/2404.17205/) (2024)
- [Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark](https://mlsanity.com/paper/2109.11243/) (2021)
- [HighlightMe: Detecting Highlights from Human-Centric Videos](https://mlsanity.com/paper/2110.01774/) (2021)
- [PERI: Part Aware Emotion Recognition In The Wild](https://mlsanity.com/paper/2210.10130/) (2022)
- [EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge](https://mlsanity.com/paper/2306.08657/) (2023)
- [Contextual Emotion Estimation from Image Captions](https://mlsanity.com/paper/2309.13136/) (2023)
- [Emotional Theory of Mind: Bridging Fast Visual Processing with Slow Linguistic Reasoning](https://mlsanity.com/paper/2310.19995/) (2023)
- [UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception](https://mlsanity.com/paper/2409.18877/) (2024)
- [Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs](https://mlsanity.com/paper/2210.04935/) (2022)

## Abstract (arXiv)

We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modalities(e.g. faces and gaits) for emotion recognition. For the second interpretation, we gather semantic context from the input image and use a self-attention-based CNN to encode this information. Finally, we use depth maps to model the third interpretation related to socio-dynamic interactions and proximity among agents. We demonstrate the efficiency of our network through experiments on EMOTIC, a benchmark dataset. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods. We also introduce a new dataset, GroupWalk, which is a collection of videos captured in multiple real-world settings of people walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is also an improvement over prior methods.