metadata

license: apache-2.0
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - Video
  - Text
size_categories:
  - 1K<n<10K

Visual Spatial Intelligence Benchmark (VSI-Bench)

This repository contains the visual spatial intelligence benchmark (VSI-Bench), introduced in Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces.

Files

The test-00000-of-00001.parquet file contains the complete dataset annotations and pre-loaded images, ready for processing with HF Datasets. It can be loaded using the following code:

from datasets import load_dataset
vsi_bench = load_dataset("nyu-visionx/VSI-Bench")

Additionally, we provide the videos in *.zip.

Dataset Description

VSI-Bench quantitatively evaluates the visual-spatial intelligence of MLLMs from egocentric video. VSI-Bench comprises over 5,000 question-answer pairs derived from 288 real videos. These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets ScanNet, ScanNet++, and ARKitScenes, and represent diverse environments -- including residential spaces, professional settings (e.g., offices, labs), and industrial spaces (e.g., factories) and multiple geographic regions. By repurposing these existing 3D reconstruction and understanding datasets, VSI-Bench benefits from accurate object-level annotations, which are used in question generation and could support future studies exploring the connection between MLLMs and 3D reconstruction.

The dataset contains the following fields:

Field Name	Description
`idx`	Global index of the entry in the dataset
`dataset`	Video source: `scannet`, `arkitscenes` or `scannetpp`
`scene_name`	Scene (video) name for each question-answer pair
`question_type`	The type of task for question
`question`	Question asked about the video
`options`	Choices for the question (only for multiple choice questions)
`ground_truth`	Ground truth answer for the question

Evaluation

VSI-Bench evaluates performance using two metrics: for multiple-choice questions, we use Accuracy, calculated based on exact matches. For numerical-answer questions, we introduce a new metric, MRA (Mean Relative Accuracy), to assess how closely model predictions align with ground truth values.

We provide an out-of-the-box evaluation of VSI-Bench in our GitHub repository, including the metrics implementation used in our framework. For further detailes, users can refer to our paper and GitHub repository.

Citation

@article{yang2024think,
    title={{Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces}},
    author={Yang, Jihan and Yang, Shusheng and Gupta, Anjali and Han, Rilyn and Fei-Fei, Li and Xie, Saining},
    year={2024},
    journal={arXiv preprint arXiv:2412.14171},
}