JoeLeelyf/OVO-Bench
OVO-Bench: How Far is Your Video-LLMs from Real-World Online VideO Understanding? 🔥🔥OVO-Bench is accepted by CVPR 2025!🔥🔥 Important Note: Current codebase is modified compared to our initial arXiv paper. We strongly recommend that any use of OVO-Bench should be based on current edition. Introduction 🌟 Three distinct problem-solving modes Backward Tracing: trace back to past events to answer the question. Real-Time Visual… See the full description on the dataset page: https://huggingface.co/datasets/JoeLeelyf/OVO-Bench.
<h1 align="center"> <font color=#0088cc>O</font><font color=#060270>V</font><font color=#0088cc>O</font>-Bench: How Far is Your Video-LLMs from Real-World <font color=#0088cc>O</font>nline <font color=#060270>V</font>ide<b style="color: #0088cc;">O</b> Understanding? </h1>
<p align="center"> 🔥🔥OVO-Bench is accepted by CVPR 2025!🔥🔥 </p>
<p align="center"> <a href="https://arxiv.org/abs/2501.05510" style="margin-right: 10px;"> <img src="https://img.shields.io/badge/arXiv-2501.05510-b31b1b.svg?logo=arXiv"> </a> </p>
Important Note: Current codebase is modified compared to our initial arXiv paper. We strongly recommend that any use of OVO-Bench should be based on current edition.
Introduction
🌟 Three distinct problem-solving modes
- Backward Tracing: trace back to past events to answer the question.
- Real-Time Visual Perception: understand and respond to events as they unfold at the current timestamp.
- Forward Active Responding: delay the response until sufficient future information becomes available to answer the question accurately.