---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- visual-question-answering
- image-classification
- object-detection
tags:
- anomaly-detection
- commonsense-reasoning
- vision-language
- benchmark
- real-world
- visual-grounding
pretty_name: "CAVE: Commonsense Anomalies in Visual Environments"
dataset_info:
  features:
    - name: image
      dtype: image
    - name: num_anomaly
      dtype: int32
    - name: image_description
      dtype: string
    - name: anomaly_description
      dtype: string
    - name: correct_version_description
      dtype: string
    - name: anomaly_explanation
      dtype: string
    - name: anomaly_justification
      dtype: string
    - name: anomaly_category
      dtype: string
    - name: severity_score
      dtype: float32
    - name: surprisal_score
      dtype: float32
    - name: complexity_score
      dtype: float32
    - name: bbox_x1
      dtype: float32
    - name: bbox_y1
      dtype: float32
    - name: bbox_x2
      dtype: float32
    - name: bbox_y2
      dtype: float32

---

# Dataset Card for CAVE: Commonsense Anomalies in Visual Environments

🏠 [Project Page](https://smontariol.github.io/cave-visual-anomalies/)  
📄 [Paper (EMNLP 2025)](https://aclanthology.org/2025.emnlp-main.1379/)  
💻 [Code](https://github.com/rishika2110/CAVE)

## Dataset Details

### Dataset Description

CAVE is the first benchmark of **real-world visual anomalies** for evaluating Vision-Language Models (VLMs). It is curated from images captured in real-life settings (photographs and screenshots taken by individuals), sourced from Reddit.

The benchmark is grounded in cognitive science literature on how humans detect and resolve anomalies. Each image is annotated with rich, multi-task annotations that support three open-ended tasks (anomaly description, explanation, and justification), one visual grounding task (anomaly localization via bounding boxes), and classification along four dimensions (anomaly category, severity, surprisal, and complexity) that characterize the anomaly.

CAVE reveals that state-of-the-art VLMs struggle substantially with visual anomaly perception and commonsense reasoning: the best model (GPT-4o) achieves only ~57% F1-score on anomaly detection even with advanced prompting strategies.

- **Curated by:** Rishika Bhagwatkar, Syrielle Montariol, Angelika Romanou, Beatriz Borges, Irina Rish, Antoine Bosselut
- **Affiliations:** EPFL, MILA
- **Language:** English
- **License:** CC-BY-4.0
- **Published at:** EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

### Dataset Sources

- **Project Page:** [https://smontariol.github.io/cave-visual-anomalies/](https://smontariol.github.io/cave-visual-anomalies/)
- **Paper:** [CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments](https://aclanthology.org/2025.emnlp-main.1379/)
- **Contact:** rishika.bhagwatkar@mila.quebec, syrielle.montariol@epfl.ch

## Uses

### Intended Uses

CAVE is designed to evaluate VLMs on their ability to:

1. **Detect** real-world commonsense anomalies in images (anomaly description).
2. **Explain** why a detected situation is anomalous (anomaly explanation).
3. **Justify** how an anomaly might have occurred (anomaly justification).
4. **Localize** anomalies within images via bounding boxes (anomaly localization).
5. **Classify** anomalies by their visual manifestation type and numerical features (severity, surprisal, complexity).

It also serves as a resource for studying the alignment between human and machine processing of visual anomalies, and for developing improved prompting strategies or fine-tuning approaches for anomaly-related tasks.

### Out-of-Scope Uses

CAVE is a benchmark for evaluation purposes. Its small size (361 images) makes it unsuitable as a training set. It should not be used to deploy anomaly detection systems in safety-critical settings without additional validation.

## Dataset Structure

### Overview

CAVE consists of **361 images**: 309 anomalous and 52 normal (non-anomalous) images. Anomalous images contain up to 3 anomalies each, totaling **334 annotated anomalies**. Each anomaly is paired with a unique bounding box.

### Annotation Fields

Each sample includes the following fields:

| Field | Description |
|---|---|
| `image` | The image (photograph or screenshot) |
| `image_description` | Short description of the image content (without describing the anomaly) |
| `anomaly_description` | Textual description of what is anomalous in the image |
| `anomaly_explanation` | Explanation of *why* the situation is anomalous (commonsense reasoning) |
| `anomaly_justification` | Plausible explanation of *how* the anomaly might have occurred |
| `anomaly_category` | Category of the anomaly's visual manifestation (see taxonomy below) |
| `bounding_box` | Coordinates of the bounding box demarcating the anomalous region |
| `severity` | 1–5 score: does the anomaly require immediate action? |
| `surprisal` | 1–5 score: how much does the situation deviate from expectations? |
| `complexity` | 1–5 score: how hard is the anomaly to detect? |

### Anomaly Category Taxonomy

Anomalies are categorized by how they visually manifest, inspired by MMBench's taxonomy of visual reasoning types:

| Category | Description | Example |
|---|---|---|
| **Entity Presence** | An object is present when it shouldn't be | A black bear in an industrial building |
| **Entity Absence** | An expected object is missing | A person using a cutter without protective gear |
| **Entity Attribute** | An object has an anomalous attribute (color, shape, label, orientation, usage) | A snack packet opened from the wrong side |
| **Spatial Relation** | An object is incorrectly positioned relative to another | Furniture blocking an emergency button |
| **Uniformity Breach** | A disruption in an expected uniform/symmetrical pattern | One tile with a different orientation |
| **Textual Anomaly** | Text in the image conveys an unexpected or contradictory message | A "KEEP RIGHT" sign with an arrow pointing left |


## Dataset Creation

Images were collected from four Reddit subreddits that specialize in content featuring unusual or uncommon situations:

- `r/ocdtriggers`
- `r/mildlyconfusing`
- `r/mildlyinfuriating`
- `r/OSHA`

The top 1,000 posts from each subreddit were downloaded using the PRAW library. 
Images were filtered through both automatic and manual processes to remove:

- Unclear or ambiguous content
- Non-realistic images
- NSFW or sensitive content
- Images with text annotations, circles, or other overlaid marks
- Images below icon resolution

Annotation proceeded in two rounds, with Amazon Mechanical Turk followed by Expert Verification & Consolidation, with 3 independent raters per anomaly for severity, surprisal, and complexity scores.


## Citation

```bibtex
@inproceedings{bhagwatkar-etal-2025-cave,
    title = "{CAVE} : Detecting and Explaining Commonsense Anomalies in Visual Environments",
    author = "Bhagwatkar, Rishika  and
      Montariol, Syrielle  and
      Romanou, Angelika  and
      Borges, Beatriz  and
      Rish, Irina  and
      Bosselut, Antoine",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1379/",
    doi = "10.18653/v1/2025.emnlp-main.1379",
    pages = "27110--27151",
}
```

## Acknowledgements

The authors acknowledge support from Canada CIFAR AI Chair Program, Canada Excellence Research Chairs Program, Swiss National Science Foundation (No. 215390), Innosuisse (PFFS-21-29), EPFL Center for Imaging, Sony Group Corporation, and a Meta LLM Evaluation Research Grant. Computational resources were provided by MILA - Quebec AI Institute.