Update app.py

app.py

@@ -250,14 +250,30 @@ def process_video(video_file, num_frames, segment_duration, num_segments):
         return [], f"Error processing video: {str(e)}", []
 
 # Create Gradio interface
-with gr.Blocks(title="Video Frame Sampling Tool") as demo:
+with gr.Blocks(title="PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill Assessment") as demo:
     gr.Markdown("""
-    # Video Frame Sampling Tool
-    
-    This tool uses an enhanced frame sampling strategy that distributes frames across temporal segments
-    of the video for better temporal coverage and content diversity.
+    # PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill Assessment
+
+    PATS (Proficiency-Aware Temporal Sampling) is a novel video sampling strategy designed specifically for automated sports skill assessment. 
+    Unlike traditional methods that randomly sample frames or use uniform intervals, PATS preserves complete fundamental movements within continuous temporal segments. 
+    The paper presenting PATS has been accepted at the 2025 4th IEEE Sport Technology and Research Workshop.
+
+    This tool showcases the PATS sampling strategy. Find out more at the project page: https://edowhite.github.io/PATS
+
+    ## Core Concept
+    The key insight is that athletic proficiency manifests through structured temporal patterns that require observing complete, uninterrupted movements. 
+    PATS addresses this by:
+
+    - **Extracting continuous temporal segments** rather than isolated frames
+    - **Preserving natural movement flow** essential for distinguishing expert from novice performance
+    - **Distributing multiple segments** across the video timeline to maximize information coverage
+
+    ## Performance
+    When applied to SkillFormer on the EgoExo4D benchmark, PATS achieves:
     
-    Upload a video and configure the sampling parameters to extract representative frames.
+    - **Consistent improvements** across all viewing configurations (+0.65% to +3.05%)
+    - **Substantial domain-specific gains:** +26.22% in bouldering, +2.39% in music, +1.13% in basketball
+
     """)
     
     with gr.Row():
@@ -308,38 +324,12 @@ with gr.Blocks(title="Video Frame Sampling Tool") as demo:
             )
             indices_output = gr.JSON(label="Frame Indices", visible=False)
     
-    # Examples
-    gr.Examples(
-        examples=[
-            [8, 4, 2.0],
-            [16, 8, 1.5],
-            [4, 2, 3.0],
-            [24, 6, 2.5],
-        ],
-        inputs=[num_frames, num_segments, segment_duration],
-        label="Example Configurations"
-    )
-    
     # Connect the processing function
     process_btn.click(
         fn=process_video,
         inputs=[video_input, num_frames, segment_duration, num_segments],
         outputs=[gallery_output, info_output, indices_output]
     )
-    
-    gr.Markdown("""
-    ### How it works:
-    1. The video is divided into the specified number of segments
-    2. Each segment has a maximum duration as specified
-    3. Frames are sampled evenly from within each segment
-    4. The algorithm ensures exactly the requested number of frames are returned
-    5. If the video is too short, it falls back to uniform sampling
-    
-    ### Tips:
-    - Use more segments for longer videos to get better temporal coverage
-    - Adjust segment duration based on the pace of content in your video
-    - For short videos, use fewer segments with shorter durations
-    """)
 
 # Launch the app
 if __name__ == "__main__":