diff --git a/css/bootstrap-4.4.1.css b/css/bootstrap-4.4.1.css
index 18fbb86..e5d90aa 100644
--- a/css/bootstrap-4.4.1.css
+++ b/css/bootstrap-4.4.1.css
@@ -5258,17 +5258,17 @@ a.badge-dark:focus, a.badge-dark.focus {
 }
 
 .jumbotron {
-  /* padding: 1rem 1rem; */
+  padding: 4rem 2rem 4rem;
   margin-bottom: 1rem;
   background-color: #ebf0f1; /*#e9ecef;*/ 
   border-radius: 0.3rem;
 }
 
-@media (min-width: 576px) {
+/* @media (min-width: 576px) {
   .jumbotron {
     padding: 4rem 2rem;
   }
-}
+} */
 
 .jumbotron-fluid {
   padding-right: 0;
diff --git a/index.html b/index.html
index b5623f5..259b48b 100644
--- a/index.html
+++ b/index.html
@@ -91,18 +91,30 @@ <h2>Abstract</h2>
 </section>
 <br>
 
+
+<style>
+  .image {
+      width: 100%;
+  }
+
+  @media (min-width: 1200px) {
+      .image {
+          width: 80%;
+      }
+  }
+</style>
 <section>
   <div class="container">
     <div class="row">
       <div class="col-12 text-center">
-        <h2>Preliminaries</h2>
+        <h2>Preliminary</h2>
         <!-- <hr style="margin-top:0px"> -->
       </div>
         <br>
         <div class="col-12">
           <p class="text-left">
             <center>
-              <img src="images/preliminary.png" style="width:75%; margin-bottom:20px;">
+              <img src="images/preliminary.png" class="image" style="margin-top:10px; margin-bottom:20px;">
             </center>
           <!-- <h4>Mean-Shift</h4> -->
           <!-- We revisit the mean-shift and introduce contrastive mean-shift (CMS) learning for generalized category discovery. -->
@@ -143,7 +155,7 @@ <h4>Learning framework: Contrastive Mean-Shift learning (CMS)</h4>
           <br>
           <h4>Validation: Estimating the number of clusters</h4>
           <center>
-            <img src="images/validation.png" style="width:80%; margin-top:10px; margin-bottom:20px;">
+            <img src="images/validation.png" class="image" style="margin-top:10px; margin-bottom:20px;">
           </center>
           During training, we estimate the number of clusters K at the end of every epoch for a fairer and efficient validation. We apply 
           agglomerative clustering on the validation set to obtain clustering results for different number of clusters. Among them, the 
@@ -154,7 +166,7 @@ <h4>Validation: Estimating the number of clusters</h4>
           <br>
           <h4>Inference: Iterative Mean-Shift (IMS)</h4>
           <center>
-            <img src="images/inference.png" style="width:80%; margin-top:10px; margin-bottom:20px;">
+            <img src="images/inference.png" class="image" style="margin-top:10px; margin-bottom:20px;">
           </center>
           To improve the final clustering property of the embeddings, we perform multi-step mean shift on the embeddings before
           agglomerative clustering. Starting from the initial embeddings from the learned encoder, we update them to $t$-step