Create README.md

README.md

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+---
+license: other
+license_name: bsd-3-clause
+license_link: https://github.com/TencentARC/TimeLens/blob/main/LICENSE
+language:
+- en
+task_categories:
+- video-text-to-text
+pretty_name: TimeLens
+size_categories:
+- 10K<n<100K
+---
+
+# TimeLens-Bench
+
+📑 [**Paper**](TODO) | 💻 [**Code**](https://github.com/TencentARC/TimeLens) | 🏠 [**Project Page**](https://timelens-arc-lab.github.io/) | 🤗 [**Model & Data**](https://huggingface.co/collections/TencentARC/TimeLens)
+
+## ✨ Dataset Description
+
+**TimeLens-Bench** is a comprehensive, high-quality evaluation benchmark for video temporal grounding, proposed in our paper [TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs](TODO).
+
+During our annotation process, we identified critical quality issues within existing datasets and performed extensive manual corrections. We observed a **dramatic re-ranking of models** on TimeLens-Bench compared to legacy benchmarks, demonstrating that TimeLens-Bench provides *more reliable* evaluation
+(more details in our [paper](TODO) and [project page](https://timelens-arc-lab.github.io/)).
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65372e922c6ef949b22c26d9/31s82GO6S5LKlW0-kcIFU.png" alt="performance_comparison_charades-1" width="35%">
+
+### Dataset Statistics
+
+The benchmark consists of manually refined versions of **three** widely used evaluation datasets for video temporal grounding:
+
+| Refined Dataset | # Videos | Avg. Duration | # Annotations | Source Dataset | Source Dataset Link |
+| :--- | :---: | :---: | :---: | :--- | :--- |
+| **Charades-TimeLens** | 1313 | 29.6 | 3363 | Charades-STA | https://github.com/jiyanggao/TALL |
+| **ActivityNet-TimeLens** | 1455* | 134.9 | 4500 | ActivityNet-Captions | https://cs.stanford.edu/people/ranjaykrishna/densevid/ |
+| **QVHighlights-TimeLens** | 1511 | 149.6 | 1541 | QVHighlights | https://github.com/jayleicn/moment_detr |
+
+<small>* To reduce the high evaluation cost from the excessively large ActivityNet Captions, we sampled videos uniformly across duration bins to curate ActivityNet-TimeLens.</small>
+
+## 🚀 Usage
+
+To download and use the benchmark for evaluation, please refer to the instructions in our [**GitHub Repository**](https://github.com/TencentARC/TimeLens#-evaluation-on-timelens-bench).
+
+## 📝 Citation
+
+If you find our work helpful for your research and applications, please cite our paper:
+
+```bibtex
+TODO
+```