Update README.md

README.md

@@ -84,18 +84,17 @@ Question and answers are provided as a json file for each task.
 
 Videos in TVBench are sourced from Perception Test, CLEVRER, STAR, MoVQA, Charades-STA, NTU RGB+D, FunQA and CSV. All videos are included in this repository, except for those from NTU RGB+D, which can be downloaded from the official [website](https://rose1.ntu.edu.sg/dataset/actionRecognition/). It is not necessary to download the full dataset, as NTU RGB+D provides a subset specifically for TVBench with the required videos. These videos are required by th Action Antonym task and should be stored in the `video/action_antonym` folder.
 
-## Leaderboard
-https://paperswithcode.com/sota/video-question-answering-on-tvbench
-
 # Citation
 If you find this benchmark useful, please consider citing:
 ```
 
-@misc{cores2024tvbench,
-  author = {Daniel Cores and Michael Dorkenwald and Manuel Mucientes and Cees G. M. Snoek and Yuki M. Asano},
-  title = {Lost in Time: A New Temporal Benchmark for Video LLMs},
-  year = {2024},
-  eprint = {arXiv:2410.07752},
+@inproceedings{Cores_2025_BMVC,
+  author    = {Daniel Cores and Michael Dorkenwald and Manuel Mucientes and Cees G. M. Snoek and Yuki M Asano},
+  title     = {Lost in Time: A New Temporal Benchmark for VideoLLMs},
+  booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
+  publisher = {BMVA},
+  year      = {2025},
+  url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_857/paper.pdf}
 }
 
 ```