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@@ -16,7 +16,7 @@ Chunyu Xie*, Bin Wang*, Fanjing Kong, Jincheng Li, Dawei Liang, Gengshen Zhang,
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  [![ICML](https://img.shields.io/badge/ICML-2025-blue.svg)](https://icml.cc/Conferences/2025)
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  <p align="center">
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- <img src="https://github.com/360CVGroup/FG-CLIP/blob/main/use_imgs/radar_chart_methods.png" width="500" height="440"/>
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  </p>
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  ## Model Framework
@@ -24,7 +24,7 @@ FG-CLIP’s training proceeds in two stages: the first stage leverages
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  global-level caption-image pairs to achieve initial fine-grained alignment, while the second stage supplements these with additional
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  region-level captions, including detailed region captions and positive/negative region descriptions to further refine the alignment.
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  <p align="center">
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- <img src="https://github.com/360CVGroup/FG-CLIP/blob/main/use_imgs/fgclip_strc.png" width=80%/>
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  </p>
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  ## Quick Start 🤗
@@ -119,7 +119,7 @@ plt.savefig("FG-CLIP/use_imgs/FGCLIP_dfcolor_cat.png")
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  ```
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  <p align="left">
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- <img src="https://github.com/360CVGroup/FG-CLIP/blob/main/use_imgs/FGCLIP_dfcolor_cat.png" width=25%/>
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  </p>
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  ## Citation
 
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  [![ICML](https://img.shields.io/badge/ICML-2025-blue.svg)](https://icml.cc/Conferences/2025)
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  <p align="center">
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+ <img src="radar_chart_methods.png" width="500" height="440"/>
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  </p>
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  ## Model Framework
 
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  global-level caption-image pairs to achieve initial fine-grained alignment, while the second stage supplements these with additional
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  region-level captions, including detailed region captions and positive/negative region descriptions to further refine the alignment.
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  <p align="center">
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+ <img src="fgclip_strc.png" width=80%/>
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  </p>
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  ## Quick Start 🤗
 
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  ```
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  <p align="left">
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+ <img src="FGCLIP_dfcolor_cat.png" width=25%/>
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  </p>
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  ## Citation