Add V-JEPA and V-JEPA 2 citations

README.md

@@ -37,7 +37,7 @@ import torch
 from transformers import AutoModel
 
 model = AutoModel.from_pretrained(
-    "epfl-neuroai/vjepa2-enoder-basic",
+    "epfl-neuroai/vjepa2-encoder-basic",
     trust_remote_code=True,
 )
 model.eval()
@@ -56,7 +56,7 @@ For decoder-only debugging, the model can also run from precomputed V-JEPA2 laye
 
 ```python
 model = AutoModel.from_pretrained(
-    "epfl-neuroai/vjepa2-enoder-basic",
+    "epfl-neuroai/vjepa2-encoder-basic",
     trust_remote_code=True,
     load_vjepa=False,
 )
@@ -85,11 +85,35 @@ This repository does not include the underlying fMRI datasets or stimulus videos
 - `config.json`, `configuration_vjepa2_fmri_encoder.py`, `modeling_vjepa2_fmri_encoder.py`: custom Transformers files for `AutoModel` loading.
 - `requirements.txt`: minimal Python dependencies.
 
+## Backbone Source
+
+The V-JEPA2 backbone weights are shipped in this repository as:
+
+```text
+vitl.pt
+```
+
+The loader uses the V-JEPA2 Torch Hub architecture with `pretrained=False`, then loads the local `vitl.pt` weights directly. This avoids relying on the moving `facebookresearch/vjepa2` Torch Hub checkpoint URL while preserving compatibility with the original decoder features. The decoder checkpoint uses canonical `extractor_config["layer_names"]` metadata.
+
 ## Citations
 
-If you use this checkpoint, please cite the source datasets:
+If you use this checkpoint, please cite the V-JEPA/V-JEPA 2 backbone papers and source datasets:
 
 ```bibtex
+@article{bardes2024revisiting,
+  title={Revisiting Feature Prediction for Learning Visual Representations from Video},
+  author={Bardes, Adrien and Garrido, Quentin and Ponce, Jean and Chen, Xinlei and Rabbat, Michael and LeCun, Yann and Assran, Mahmoud and Ballas, Nicolas},
+  journal={arXiv preprint arXiv:2404.08471},
+  year={2024}
+}
+
+@article{assran2025vjepa2,
+  title={V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning},
+  author={Assran, Mido and Bardes, Adrien and Fan, David and Garrido, Quentin and Howes, Russell and Komeili, Mojtaba and Muckley, Matthew and Rizvi, Ammar and Roberts, Claire and Sinha, Koustuv and others},
+  journal={arXiv preprint arXiv:2506.09985},
+  year={2025}
+}
+
 @article{tang2025diverse,
   title={Diverse perceptual representations across visual pathways emerge from a single objective},
   author={Tang, Yingtian and Gokce, Abdulkadir and Al-Karkari, Khaled Jedoui and Yamins, Daniel and Schrimpf, Martin},