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license: cc-by-4.0
pretty_name: MLLM Visual Emotion
task_categories:
- image-classification
- visual-question-answering
language:
- en
tags:
- emotion-recognition
- facial-expression
- multimodal
- large-language-models
- benchmark
- visual-emotion-analysis
size_categories:
- 1K-10K
viewer: false
configs:
- config_name: default
data_files: []
---
# MLLMVisualEmotion
> **Reading Human Emotion Through Machine Eyes**
This repository accompanies the study *"Reading Human Emotion Through Machine Eyes"* on visual emotion recognition by multimodal large language models (MLLMs). It contains the newly constructed **FaceEmotion** dataset, the preprocessed **Emotion6** dataset, outcome tables, source data underlying all figures, and the complete model outputs for all tasks across all scenarios.
## Repository Structure
```
MLLMVisualEmotion/
├── datasets/
│ ├── Emotion6/
│ │ └── processed/
│ │ ├── emotion_dataset.json
│ │ ├── images_multiple_choice_questions.json
│ │ ├── images_judgement_questions.json
│ │ ├── images_marked_multiple_choice_questions.json
│ │ ├── images_could_judgement_questions.json
│ │ ├── images_really_judgement_questions.json
│ │ ├── images_emotional_tendency_multiple_choice_questions.json
│ │ ├── images_emotional_tendency_judgement_questions.json
│ │ └── imgs/
│ └── FaceEmotion/
│ └── processed/
│ ├── emotion_dataset.json
│ ├── images_multiple_choice_questions.json
│ ├── images_judgement_questions.json
│ ├── images_marked_multiple_choice_questions.json
│ ├── images_could_judgement_questions.json
│ ├── images_really_judgement_questions.json
│ ├── images_emotional_tendency_multiple_choice_questions.json
│ ├── images_emotional_tendency_judgement_questions.json
│ └── imgs/
├── model_outputs/
│ ├── Undeterminable_results.json
│ ├── Gemini3p1Flash/
│ ├── GPT4o/
│ ├── GPT5/
│ ├── InternVL2/
│ ├── InternVL3/
│ ├── LLaVA1p5/
│ ├── LLaVA1p6/
│ ├── Qwen2/
│ ├── Qwen2p5/
│ └── Qwen3/
└── Source_Data.xlsx
```
## Datasets
### Emotion6 (Preprocessed)
The Emotion6 dataset is a widely used benchmark for visual emotion recognition, originally consisting of 1,980 images collected from Flickr. Each image is annotated with one of seven emotion categories (*Anger, Disgust, Fear, Joy, Neutral, Sadness, Surprise*) and an arousal value on a 1–9 scale.
- **`emotion_dataset.json`** — Image filenames, emotion labels, and arousal values.
- **`imgs/`** — The image files.
### FaceEmotion (Newly Constructed)
FaceEmotion is a newly constructed dataset of 1,000 facial expression images, each annotated with one of seven emotion categories (*Anger, Disgust, Fear, Joy, Neutral, Sadness, Surprise*).
- **`emotion_dataset.json`** — Image filenames and emotion labels.
- **`imgs/`** — The image files.
### Task Input Files
For each dataset, the following JSON files contain the formatted prompts used as inputs to the MLLMs:
| File | Task Description |
|------|-----------------|
| `images_multiple_choice_questions.json` | Multi-class emotion selection (7-way classification) |
| `images_judgement_questions.json` | Binary emotion judgement (Yes/No per emotion category) |
| `images_marked_multiple_choice_questions.json` | Multi-class emotion selection with contextual cues |
| `images_could_judgement_questions.json` | Binary emotion judgement with "Could" contextual framing |
| `images_really_judgement_questions.json` | Binary emotion judgement with "really" contextual framing |
| `images_emotional_tendency_multiple_choice_questions.json` | Multi-class emotion selection with emotional tendency framing |
| `images_emotional_tendency_judgement_questions.json` | Binary emotion judgement with emotional tendency framing |
Each entry in these files contains:
- **Image identifier** and path
- **Ground-truth label**
- **Experiment setup** identifier
- **Query** (the full prompt sent to the model)
- **Answer** (the ground-truth answer)
- **Arousal** value (Emotion6 only)
## Model Outputs
The `model_outputs/` directory contains the raw outputs from 10 MLLMs evaluated on all tasks:
| Model | Directory |
|-------|-----------|
| Gemini 3.1 Flash | `Gemini3p1Flash/` |
| GPT-4o | `GPT4o/` |
| GPT-5 | `GPT5/` |
| InternVL2 | `InternVL2/` |
| InternVL3 | `InternVL3/` |
| LLaVA 1.5 | `LLaVA1p5/` |
| LLaVA 1.6 | `LLaVA1p6/` |
| Qwen2-VL | `Qwen2/` |
| Qwen2.5-VL | `Qwen2p5/` |
| Qwen3-VL | `Qwen3/` |
Each model directory contains two subdirectories:
- **`Emotion6/`** — Results on the natural-scene Emotion6 dataset.
- **`FaceEmotion/`** — Results on the facial-expression FaceEmotion dataset.
Within each subdirectory, result files mirror the task input files:
| File | Task Description |
|------|-----------------|
| `images_multiple_choice_results.json` | Multi-class emotion selection results |
| `images_judgement_results.json` | Binary emotion judgement results |
| `images_marked_multiple_choice_results.json` | Multi-class selection with contextual cues results |
| `images_could_judgement_results.json` | Binary judgement with "Could" framing results |
| `images_really_judgement_results.json` | Binary judgement with "really" framing results |
| `images_emotional_tendency_multiple_choice_results.json` | Multi-class selection with emotional tendency results |
| `images_emotional_tendency_judgement_results.json` | Binary judgement with emotional tendency results |
Each result entry extends the input entry with:
- **`AI_answer`** — The model's predicted answer.
- **`accuracy`** — Binary accuracy score (1 = correct, 0 = incorrect).
- **`model_output`** — The full raw text output from the model.
### Undeterminable Results
**`Undeterminable_results.json`** records, for each model and each scenario (Emotion6 / FaceEmotion), the count and frequency of "Undeterminable" responses in the multi-class emotion selection task that includes an "Undeterminable" option.
## Source Data
**`Source_Data.xlsx`** contains the outcome tables and source data underlying all figures presented in the study.
## License
This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
## Citation
If you use this dataset in your research, please cite the corresponding paper.
## Contact
For questions or issues, please open a discussion on the Hugging Face repository. |