Update README.md

README.md

@@ -1,3 +1,44 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- mean_iou
+tags:
+- Semantic_Future_Prediction
+---
+
+# Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers
+
+![teaser.png](https://cdn-uploads.huggingface.co/production/uploads/677272184d148b904333e874/nm6D0QZpagySIkWDg7Y4h.png)
+
+
+FUTURIST employs a multimodal visual sequence transformer to directly predict multiple future semantic modalities. We focus on two key modalities: semantic segmentation and depth estimation.
+
+- Key innovation 1: We introduce a VAE-free hierarchical tokenization process integrated directly into our transformer. This simplifies training, reduces computational overhead, and enables true end-to-end optimization
+- Key innovation 2: Our model features an efficient cross-modality fusion mechanism that improves predictions by learning synergies between different modalities (segmentation + depth)
+- Key innovation 3: We developed a novel multimodal masked visual modeling objective specifically designed for future prediction tasks
+
+We achieve state-of-the-art performance in future semantic segmentation on Cityscapes, with strong improvements in both short-term (0.18s) and mid-term (0.54s) predictions
+
+
+# Code
+https://github.com/Sta8is/FUTURIST
+
+# Demo:
+We provide 2 quick demos.
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1fS51KGb1nwDiLplVcM4stypQe3Qtb5iW?usp=sharing)
+- [Demo](https://github.com/Sta8is/FUTURIST/blob/main/demo.ipynb).
+
+# Citation:
+If you found Futurist useful in your research, please consider starring ⭐ us on GitHub and citing 📚 us in your research!
+```
+@InProceedings{Karypidis_2025_CVPR,
+author = {Karypidis, Efstathios and Kakogeorgiou, Ioannis and Gidaris, Spyros and Komodakis, Nikos},
+title = {Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers},
+booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
+month = {June},
+year = {2025},
+pages = {3793-3803}
+```