Update README.md

README.md

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 <img src='assets/visual_abstract.png' height="50%" width="50%">
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-PETALface, is the first work which uses image-quality adaptive LoRA layers for low-resolution face recgonition. The main contributions of our work are:
+PETAL<i>face</i> is the first work which uses image-quality adaptive LoRA layers for low-resolution face recgonition. 
+The main contributions of our work are:
 1. We introduce the use of the LoRA-based PETL technique to adapt large pre-trained face-recognition models to low-resolution datasets. 
 2. We propose an image-quality-based weighting of LoRA modules to create separate proxy encoders for high-resolution and low-resolution data, 
 ensuring effective extraction of embeddings for face recognition. 
-3. We demonstrate the superiority of PETAL\textit{face} in adapting to low-resolution datasets, outperforming other state-of-the-art models on 
+3. We demonstrate the superiority of PETAL<i>face</i> in adapting to low-resolution datasets, outperforming other state-of-the-art models on 
 low-resolution benchmarks while maintaining performance on high-resolution and mixed-quality datasets.
 
 
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 <img src='assets/petalface.png'>
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-Overview of the proposed PETALface approach: We include an additional trainable module in linear layers present in attention layers and the 
+Overview of the proposed PETAL<i>face</i> approach: We include an additional trainable module in linear layers present in attention layers and the 
 final feature projection MLP. The trainable module is highlighted on the right. Specifically, we add two LoRA layers, where the weightage α is 
 decided based on the input-image quality, computed using an off-the-shelf image quality assessment network (IQA).