Committing local changes before pull

README.md

@@ -1,3 +1,71 @@
 ---
 license: apache-2.0
+language:
+- en
+tags:
+- vlm
+- reasoning
+- multimodal
+- nli
+size_categories:
+- n<1K
+task_categories:
+- visual-question-answering
 ---
+
+# **NL-Eye Benchmark**
+
+Will a Visual Language Model (VLM)-based bot warn us about slipping if it detects a wet floor? 
+Recent VLMs have demonstrated impressive capabilities, yet their ability to infer outcomes and causes remains underexplored. To address this, we introduce **NL-Eye**, a benchmark designed to assess VLMs' **visual abductive reasoning skills**. 
+NL-Eye adapts the **abductive Natural Language Inference (NLI)** task to the visual domain, requiring models to evaluate the **plausibility of hypothesis images** based on a premise image and explain their decisions. The dataset contains **350 carefully curated triplet examples** (1,050 images) spanning diverse reasoning categories, temporal categories and domains.
+NL-Eye represents a crucial step toward developing **VLMs capable of robust multimodal reasoning** for real-world applications, such as accident-prevention bots and generated video verification.
+
+project page: [NL-Eye project page](https://venturamor.github.io/NLEye/)
+
+preprint: [NL-Eye arxiv](https://arxiv.org/abs/2410.02613)
+
+---
+
+## **Dataset Structure**
+The dataset contains:
+- A **CSV file** with annotations (`test_set.csv`).
+- An **images directory** with subdirectories for each sample.
+
+### **CSV Fields:**
+| Field                          | Type     | Description                                                    |
+|--------------------------------|----------|----------------------------------------------------------------|
+| `sample_id`                    | `int`    | Unique identifier for each sample.                             |
+| `reasoning_category`           | `string` | One of the six reasoning categories (physical, functional, logical, emotional, cultural, or social). |
+| `domain`                       | `string` | One of the ten domain categories (e.g., education, technology).     |
+| `time_direction`               | `string` | One of three directions (e.g., forward, backward, parallel).                 |
+| `time_duration`                | `string` | One of three durations (e.g., short, long, parallel).                  |
+| `premise_description`          | `string` | Description of the premise.                               |
+| `plausible_hypothesis_description` | `string` | Description of the plausible hypothesis.                        |
+| `implausible_hypothesis_description` | `string` | Description of the implausible hypothesis.                      |
+| `gold_explanation`             | `string` | The gold explanation for the sample's plausibility.             |
+| `additional_valid_human_explanations` | `string` (optional) | Extra human-generated (crowd-workers) explanations for explanation diversity. |
+
+> **Note**: Not all samples contain `additional_valid_human_explanations`.
+
+---
+
+### **Images Directory Structure:**
+The `images/` directory contains **subdirectories named after each `sample_id`**. Each subdirectory includes:
+- **`premise.png`**: Image showing the premise.
+- **`hypothesis1.png`**: Plausible hypothesis.
+- **`hypothesis2.png`**: Implausible hypothesis.
+
+## **Usage**
+This dataset is **only for test purposes**. 
+
+### Citation
+```bibtex
+@misc{ventura2024nleye,
+  title={NL-Eye: Abductive NLI for Images},
+  author={Mor Ventura and Michael Toker and Nitay Calderon and Zorik Gekhman and Yonatan Bitton and Roi Reichart},
+  year={2024},
+  eprint={2410.02613},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+