Improve model card metadata and content

README.md

@@ -1,25 +1,22 @@
 ---
 license: mit
-pipeline_tag: video-classification
+pipeline_tag: image-classification
 tags:
-  - deepfake-detection
-  - face-forensics
-  - person-of-interest
-  - masked-autoencoder
-  - arxiv:2606.20302
+- deepfake-detection
+- face-forensics
+- person-of-interest
+- masked-autoencoder
 ---
 
 # CUPID — Person-of-Interest Deepfake Detection
 
-Weights for [CUPID](https://github.com/polimi-ispl/CUPID): *Reconstructing UV
-Texture Maps for Interpretable Person-of-Interest Deepfake Detection*.
+Weights for **CUPID**: *Reconstructing UV Texture Maps for Interpretable Person-of-Interest Deepfake Detection*.
 
-📄 **Paper:** [arXiv:2606.20302](https://arxiv.org/abs/2606.20302)
+📄 **Paper:** [arXiv:2606.20302](https://arxiv.org/abs/2606.20302)  
+**Authors:** Giovanni Affatato, Sara Mandelli, Edoardo Daniele Cannas, Paolo Bestagini, Stefano Tubaro.  
+💻 **Code:** [polimi-ispl/CUPID](https://github.com/polimi-ispl/CUPID)
 
-`cupid_mae.pth` is a ViT-Tiny Masked Autoencoder trained self-supervised on UV
-face textures of real videos (VoxCeleb2). The CUPID pipeline scores a test
-video by max cosine similarity between its CLS-token features and those of
-reference videos of the person of interest.
+`cupid_mae.pth` is a ViT-Tiny Masked Autoencoder trained self-supervised on UV face textures of real videos (VoxCeleb2). The CUPID pipeline scores a test video by max cosine similarity between its CLS-token features and those of reference videos of the person of interest.
 
 ## Usage
 
@@ -33,14 +30,7 @@ Weights are downloaded automatically on first use.
 
 ## Third-party weights
 
-CUPID's UV-texture extraction uses four asset files from
-[3DDFA_V3](https://github.com/wang-zidu/3DDFA-V3) (CVPR 2024), which are NOT
-mirrored here. They are downloaded directly from the authors' repository at
-[Zidu-Wang/3DDFA-V3](https://huggingface.co/datasets/Zidu-Wang/3DDFA-V3) and
-remain subject to their respective licenses and provenance (RetinaFace
-weights from biubug6/Pytorch_Retinaface, large_base_net.pth from HRN,
-net_recon.pth from 3DDFA_V3, face_model.npy derived from the Basel Face
-Model, Exp_Pca, and Deep3D).
+CUPID's UV-texture extraction uses four asset files from [3DDFA_V3](https://github.com/wang-zidu/3DDFA-V3) (CVPR 2024), which are NOT mirrored here. They are downloaded directly from the authors' repository at [Zidu-Wang/3DDFA-V3](https://huggingface.co/datasets/Zidu-Wang/3DDFA-V3) and remain subject to their respective licenses and provenance (RetinaFace weights from biubug6/Pytorch_Retinaface, large_base_net.pth from HRN, net_recon.pth from 3DDFA_V3, face_model.npy derived from the Basel Face Model, Exp_Pca, and Deep3D).
 
 ## License
 
@@ -50,10 +40,9 @@ The CUPID checkpoint is released under the MIT license.
 
 ```bibtex
 @article{affatato2026cupid,
-  title   = {{CUPID}: Reconstructing UV Texture Maps for Interpretable
-             Person-of-Interest Deepfake Detection},
-  author  = {Affatato, Giovanni and Mandelli, Sara and Bestagini, Paolo and Tubaro, Stefano},
+  title   = {{CUPID}: Reconstructing UV Texture Maps for Interpretable Person-of-Interest Deepfake Detection},
+  author  = {Affatato, Giovanni and Mandelli, Sara and Cannas, Edoardo Daniele and Bestagini, Paolo and Tubaro, Stefano},
   journal = {arXiv preprint arXiv:2606.20302},
   year    = {2026},
 }
-```
+```