Update README.md

README.md

@@ -172,13 +172,13 @@ wget https://huggingface.co/TIGER-Lab/UniIR/resolve/main/checkpoint/CLIP_SF/clip
 # Clone the GENIUS checkpoints (Stage 1 and 2)
 git clone https://huggingface.co/Sungyeon/GENIUS
 ```
-### Each Checkpoints
+### Each Component Checkpoints
 - **CLIP-SF Model** (Stage 0): [`clip_sf_large.pth`](https://huggingface.co/TIGER-Lab/UniIR/blob/main/checkpoint/CLIP_SF/clip_sf_large.pth)
 - **Residual Quantization Model** (Stage 1): [`rq_clip_large.pth`](https://huggingface.co/Sungyeon/GENIUS/blob/main/checkpoint/rq_clip_large.pth)
 - **Generator Model** (Stage 2): [`GENIUS_t5small.pth`](https://huggingface.co/Sungyeon/GENIUS/blob/main/checkpoint/GENIUS_t5small.pth)
 
-> Note: All three models are required for full functionality. The CLIP-SF model is used for feature extraction, the Residual Quantization model for ID encoding, and the Generator model for retrieval.
-
+> Note: All three models are required for full functionality.
+> 
 ## 📈 Performance
 
 > The results in parentheses denote scores from our reimplemented checkpoints, as the originals were lost during server migration. While close to the paper, slight variations may occur due to retraining randomness.
@@ -218,7 +218,7 @@ When the candidate pool grows, embedding‐based retrieval (e.g., CLIP + nearest
 If you find this work useful, please cite:
 
 ```bibtex
-@article{kim2024genius,
+@inproceedings{kim2024genius,
   title={GENIUS: A Generative Framework for Universal Multimodal Search},
   author={Kim, Sungyeon and Zhu, Xinliang and Lin, Xiaofan and Bastan, Muhammet and Gray, Douglas and Kwak, Suha},
   journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},