HRVideoBench

This repo contains the test data for HRVideoBench, which is released under the paper "VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation". VISTA is a video spatiotemporal augmentation method that generates long-duration and high-resolution video instruction-following data to enhance the video understanding capabilities of video LMMs.

🌐 Homepage | 📖 arXiv | 💻 GitHub | 🤗 VISTA-400K | 🤗 Models | 🤗 HRVideoBench

HRVideoBench Overview

We observe that existing video understanding benchmarks are inadequate for accurately assessing the ability of video LMMs to understand high-resolution videos, especially the details inside the videos. Prior benchmarks mainly consist of low-resolution videos. More recent benchmarks focus on evaluating the long video understanding capability of video LMMs, which contain questions that typically pertain to a short segment in the long video. As a result, a model's high-resolution video understanding performance can be undermined if it struggles to sample or retrieve the relevant frames from a lengthy video sequence.

To address this gap, we introduce HRVideoBench, a comprehensive benchmark with 200 multiple-choice questions designed to assess video LMMs for high-resolution video understanding. HRVideoBench focuses on the perception and understanding of small regions and subtle actions in the video. Our test videos are at least 1080p and contain 10 different video types collected with real-world applications in mind. For example, key applications of high-resolution video understanding include autonomous driving and video surveillance. We correspondingly collect POV driving videos and CCTV footage for the benchmark. Our benchmark consists of 10 types of questions, all of which are manually annotated and can be broadly categorized into object and action-related tasks. Examples of HRVideoBench questions are shown in the figure below.

Usage

We release the original video (under the folder videos) and the extracted JPEG video frames (frames.zip) in this repo. To access the 200 test questions, please refer to hrvideobench.jsonl.

Citation

If you find our paper useful, please cite us with

@misc{ren2024vistaenhancinglongdurationhighresolution,
      title={VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation}, 
      author={Weiming Ren and Huan Yang and Jie Min and Cong Wei and Wenhu Chen},
      year={2024},
      eprint={2412.00927},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.00927}, 
}