Update README.md

README.md

@@ -31,6 +31,7 @@ task_categories:
 
 ## 🔥 News
 
+* **`2025.09.18`** 🎉 FAVOR-Bench has been accepted by **NeurIPS 2025 Datasets and Benchmarks Track**!
 * **`2025.03.19`** 🌟 We released Favor-Bench, a new benchmark for fine-grained video motion understanding that spans both ego-centric and third-person perspectives with comprehensive evaluation including both close-ended QA tasks and open-ended descriptive tasks!
 
 ## Introduction
@@ -119,12 +120,11 @@ More data statistics of FAVOR-Bench. Left: Index distribution of correct answers
 If you find our work helpful for your research, please consider citing our work.
 
 ```bibtex
-@misc{tu2025favor,
-      title={FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding},
-      author={Chongjun Tu and Lin Zhang and Pengtao Chen and Peng Ye and Xianfang Zeng and Wei Cheng and Gang Yu and Tao Chen},
-      year={2025},
-      eprint={2503.14935},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
+@article{tu2026favor,
+  title={Favor-bench: A comprehensive benchmark for fine-grained video motion understanding},
+  author={Tu, Chongjun and Zhang, Lin and Ye, Peng and Zeng, Xianfang and Cheng, Wei and Yu, Gang and Chen, Tao and others},
+  journal={Advances in Neural Information Processing Systems},
+  volume={38},
+  year={2026}
 }
 ```