Update README.md

README.md

@@ -4,7 +4,7 @@ language:
 - en
 ---
 
-# PETAL<i>face</i> Model Card
+# <i>SegFace</i> Model Card
 
 <div align="center">
 
@@ -15,10 +15,6 @@ language:
 
 ## Introduction
 
-<div  align="center">
-<img src='assets/visual_abstract.png' height="50%" width="50%">
-</div>
-
 The key contributions of our work are,
 1. We introduce a lightweight transformer decoder with learnable class-specific tokens, that ensures each token is dedicated to a specific class, thereby enabling independent modeling of classes. The design effectively addresses the challenge of poor segmentation performance of long-tail classes, prevalent in existing methods. 
 2. Our multi-scale feature extraction and MLP fusion strategy, combined with a transformer decoder that leverages learnable class-specific tokens, mitigates the dominance of head classes during training and enhances the feature representation of long-tail classes. 
@@ -28,7 +24,7 @@ The key contributions of our work are,
 
 ## Training Framework
 <div  align="center">
-<img src='assets/segface'>
+<img src='assets/segface.png'>
 </div>
 
 The proposed architecture, <i>SegFace</i>, addresses face segmentation by enhancing the performance on long-tail classes through a transformer-based approach. Specifically, multi-scale features are first extracted from an image encoder and then fused using an MLP fusion module to form face tokens. These tokens, along with class-specific tokens, undergo self-attention, face-to-token, and token-to-face cross-attention operations, refining both class and face tokens to enhance class-specific features. Finally, the upscaled face tokens and learned class tokens are combined to produce segmentation maps for each facial region.