relation section

index.html

@@ -201,60 +201,25 @@
             plug-and-play model, BEYOND can easily cooperate with the Adversarial Trained Classifier (ATC), achieving state-of-the-art 
             (SOTA) robustness accuracy. Experimental results show that BEYOND outperforms baselines by a large margin, especially under 
             adaptive attacks. Empowered by the robust relationship built on SSL, we found that BEYOND outperforms baselines in terms 
-            of both detection ability and speed
+            of both detection ability and speed.
           </p>
-          <!-- <p>
-            We present the first method capable of photorealistically reconstructing a non-rigidly
-            deforming scene using photos/videos captured casually from mobile phones.
-          </p>
-          <p>
-            Our approach augments neural radiance fields
-            (NeRF) by optimizing an
-            additional continuous volumetric deformation field that warps each observed point into a
-            canonical 5D NeRF.
-            We observe that these NeRF-like deformation fields are prone to local minima, and
-            propose a coarse-to-fine optimization method for coordinate-based models that allows for
-            more robust optimization.
-            By adapting principles from geometry processing and physical simulation to NeRF-like
-            models, we propose an elastic regularization of the deformation field that further
-            improves robustness.
-          </p>
-          <p>
-            We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
-            photos/videos into deformable NeRF
-            models that allow for photorealistic renderings of the subject from arbitrary
-            viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
-            using a
-            rig with two mobile phones that take time-synchronized photos, yielding train/validation
-            images of the same pose at different viewpoints. We show that our method faithfully
-            reconstructs non-rigidly deforming scenes and reproduces unseen views with high
-            fidelity.
-          </p> -->
         </div>
       </div>
     </div>
     <!--/ Abstract. -->
-
-    <!-- Paper video. -->
-    <!-- <div class="columns is-centered has-text-centered">
-      <div class="column is-four-fifths">
-        <h2 class="title is-3">Video</h2>
-        <div class="publication-video">
-          <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&amp;showinfo=0"
-                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-        </div>
-      </div>
-    </div> -->
-    <!--/ Paper video. -->
   </div>
 </section>
 
 <section class="section">
   <div class="container is-max-desktop">
-    <h2 class="title is-3">Introduction</h2>
+    <h2 class="title is-3">Neighborhood Relations of Benign Examples and AEs</h2>
     <div class="columns is-centered">
-      <div class="column has-text-centered">
-
+      <div class="column container-centered">
+          <img src="./static/images/relations.png" alt="Neighborhood Relations of Benign Examples and AEs"/>
+          <p><strong>Figure 1. Neighborhood Relations of Benign Examples and AEs.</strong>. First, we augment the input image to obtain a bunch of its neighbors. Then, we
+            perform the label consistency detection mechanism on the classifier’s prediction of the input image and that of neighbors predicted by
+            SSL’s classification head. Meanwhile, the representation similarity mechanism employs cosine distance to measure the similarity among
+            the input image and its neighbors. Finally, The input image with poor label consistency or representation similarity is flagged as AE.</p>
       </div>
     </div>
   </div>
@@ -265,9 +230,8 @@
     <h2 class="title is-3">Method Overview of BEYOND</h2>
     <div class="columns is-centered">
       <div class="column container-centered">
-        <img src="./static/images/overview.png" class="method_overview"
-        alt="Method Overview of BEYOND"/>
-        <p><strong>Figure 1.</strong> Overview of <strong>BEYOND</strong>. First, we augment the input image to obtain a bunch of its neighbors. Then, we
+        <img src="./static/images/overview.png" alt="Method Overview of BEYOND"/>
+        <p><strong>Figure 2. Overview of BEYOND.</strong>. First, we augment the input image to obtain a bunch of its neighbors. Then, we
           perform the label consistency detection mechanism on the classifier’s prediction of the input image and that of neighbors predicted by
           SSL’s classification head. Meanwhile, the representation similarity mechanism employs cosine distance to measure the similarity among
           the input image and its neighbors. Finally, The input image with poor label consistency or representation similarity is flagged as AE.</p>