merge

.gitattributes

@@ -38,3 +38,5 @@ images/biasexplorer/painter[[:space:]](Dalle2[[:space:]]vs[[:space:]]SD2).png fi
 images/biasexplorer/waitress[[:space:]](Dalle2[[:space:]]vs[[:space:]]SD1.4).png filter=lfs diff=lfs merge=lfs -text
 images/bovw/librarians.png filter=lfs diff=lfs merge=lfs -text
 images/colorfulness/nativeamerican_man.png filter=lfs diff=lfs merge=lfs -text
+images/bovw/bookshelves.png filter=lfs diff=lfs merge=lfs -text
+images/bovw/gibberish.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -205,12 +205,12 @@ with gr.Blocks() as demo:
             gr.HTML(
                 """
          <h4>Bag of Visual Words</h4>
-         <p style="margin-bottom: 14px; font-size: 100%">  Another way of providing the means for a structured traversal of the dataset is a nearest-neighbor explorer based on visual features provided by an image's SIFT features, which we quantize into a visual vocabulary to represent the entire image dataset as a TF-IDF matrix. These tools are especially useful in honing in on stereotypical content that is often encoded visually, but also failure modes of the model such as the misinterpetation of the "stocker" profession as an imagined dog-breed. The screenshot to the right shows how SIFT visual patterns tend to cluster together, namely in this instance the bookshelf in the background. </p>
-        """
-            )
-            gr.Image("images/bovw/librarians.png")
-    gr.Markdown(
-        """
+         <p style="margin-bottom: 14px; font-size: 100%">  Another way of providing the means for a structured traversal of the dataset is a nearest-neighbor explorer based on visual features provided by an image's SIFT features, which we quantize into a visual vocabulary to represent the entire image dataset as a TF-IDF matrix. These tools are especially useful in honing in on stereotypical content that is often encoded visually, but also failure modes of the model such as the misinterpetation of the "stocker" profession as an imagined dog-breed. The screenshot to the right shows how SIFT visual patterns tend to cluster together, namely in this instance the booksheves in the background, or the gibberish pseudo-English text that often plagues TTI systems. </p>
+        """)    
+            with gr.Column():
+                gr.Image("images/bovw/bookshelves.png")
+                gr.Image("images/bovw/gibberish.png")
+    gr.Markdown("""
                 ### All of the tools created as part of this project:
             """
     )