Update README.md

README.md

@@ -16,6 +16,7 @@ MovieChat can handle videos with >10K frames on a 24GB graphics card. MovieChat
 <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.</h5>
 
 ## :fire: News
+* **[2024.4.29]** :page_with_curl: We update the MovieChat+ [paper](https://arxiv.org/abs/2404.17176) with implementation details, technical evaluations, and dataset information.
 * **[2024.4.25]** :keyboard:We update a new version of MovieChat+. We realse the [MovieChat+ code](https://github.com/rese1f/MovieChat/blob/main/MovieChat/models/moviechat%2B.py) and the corresponding [evaluation code](https://github.com/rese1f/MovieChat/blob/main/eval_code/result_prepare/run_inference_qa_moviechat%2B.py). Our paper is Coming soon!
 * **[2024.4.19]** :keyboard:We update the latest source code of MovieChat to [PyPI](https://pypi.org/). Now you can use MovieChat by `pip install Moviechat` directly!
 * **[2024.3.25]** :bar_chart: We host challenge track 1 of [the 4th International Workshop on Long-form Video Understanding: Towards Multimodal AI Assistant and Copilot](https://cvpr.thecvf.com/Conferences/2024/workshop-list) at CVPR 2024. You can participate in the challenge and submit your results via [Codalab](https://codalab.lisn.upsaclay.fr/competitions/18284?secret_key=bd5e312c-4775-43cf-933b-70726d00bcbe). We will display the results on the [leaderboard](https://espere-1119-song.github.io/LOVEU-CVPR-24-Track-1-Leaderboard/). For each participant, we hope you can submit your results in JSON format and report both the average running time and VRAM usage. We will use these metrics to select the most efficient method. For detailed information about the challenge, please refer to this [link](https://sites.google.com/view/loveucvpr24/track1).