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@@ -55,7 +55,7 @@ <h1>LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation</h1>
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 <!--                    <span style="font-size:22px"><a href="https://github.com/koutilya-pnvr/SharinGAN">Code</a></span>    -->
 <!--                    <span style="font-size:22px"><a href="https://openaccess.thecvf.com/content_CVPR_2020/html/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.html">Paper [CVPR 2020] </a> </span>    -->
-                    <span style="font-size:22px"><a href="https://arxiv.org/abs/2303.12343">Paper [Arxiv]</a></span>  
+                    <span style="font-size:30px"><a href="https://arxiv.org/abs/2303.12343">Paper [Arxiv]</a> &nbsp &nbsp <a href="https://github.com/koutilya-pnvr/LD-ZNet">Code (coming soon)</a> &nbsp &nbsp <a href="https://drive.google.com/drive/folders/1oZDJu5Y7nqN23Fcb6kCvXy1Do69l_YkQ?usp=sharing">AIGI dataset</a></span> 
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@@ -149,6 +149,8 @@ <h2>Method</h2>
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 	<p>We create a dataset consisting of AI-generated images which we name <strong>AIGI</strong> dataset, to showcase the usefulness of our approach for text-based segmentation on a different domain. We use 100 AI-generated images from <a href="https://lexica.art/">lexica.art</a> and manually annotated multiple regions for 214 text-prompts relevant to these images, as shown in Figure 3 above.</p>
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+<p>The AIGI dataset is made public here: <a href="https://drive.google.com/drive/folders/1oZDJu5Y7nqN23Fcb6kCvXy1Do69l_YkQ?usp=sharing">Google Drive Link</a></p>
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