metadata

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

preprint: NL-Eye arxiv

Dataset Structure

The dataset contains:

A CSV file with annotations (test_set.csv).
An images directory with subdirectories for each sample (images/).

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

@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}
}