README.md

---
license: mit
size_categories:
  - 1K<n<10K
configs:
  - config_name: CaReBench
    data_files:
      - split: test
        path: json/metadata.json
---

<div align="center">
  <h1 style="margin: 0">
    <img src="assets/logo.png" style="width:1.5em; vertical-align: middle; display: inline-block; margin: 0" alt="Logo">
    <span style="vertical-align: middle; display: inline-block; margin: 0"><b>CaReBench: A Fine-grained Benchmark for Video Captioning and Retrieval</b></span>
  </h1>
  
  <p style="margin: 0">
    Yifan Xu, <a href="https://scholar.google.com/citations?user=evR3uR0AAAAJ">Xinhao Li</a>, Yichun Yang, Desen Meng, Rui Huang, <a href="https://scholar.google.com/citations?user=HEuN8PcAAAAJ">Limin Wang</a>
  </p>
  
  <p align="center">
    🤗 <a href="https://huggingface.co/MCG-NJU/CaRe-7B">Model</a> &nbsp&nbsp | &nbsp&nbsp 🤗 <a href="https://huggingface.co/datasets/MCG-NJU/CaReBench">Data</a> &nbsp&nbsp｜ &nbsp&nbsp 📑 <a href="https://arxiv.org/pdf/2501.00513">Paper</a> &nbsp&nbsp 
  </p>
</div>

![](assets/comparison.png)

## 📝 Introduction

**🌟 CaReBench** is a fine-grained benchmark comprising **1,000 high-quality videos** with detailed human-annotated captions, including **manually separated spatial and temporal descriptions** for independent spatiotemporal bias evaluation.
![CaReBench](assets/carebench.png)

**📊 ReBias and CapST Metrics** are designed specifically for retrieval and captioning tasks, providing a comprehensive evaluation framework for spatiotemporal understanding in video-language models.


**⚡ CaRe: A Unified Baseline** for fine-grained video retrieval and captioning, achieving competitive performance through **two-stage Supervised Fine-Tuning (SFT)**. CaRe excels in both generating detailed video descriptions and extracting robust video features.
![CaRe Training Recipe](assets/care_model.png)

**🚀 State-of-the-art performance** on both detailed video captioning and fine-grained video retrieval. CaRe outperforms CLIP-based retrieval models and popular MLLMs in captioning tasks.
![alt text](assets/performance.png)