update

app.py

@@ -48,9 +48,9 @@ with block:
             refresh_button = gr.Button("Refresh")
             refresh_button.click(fn=refresh_data, outputs=data_component)
 
-        # table 2
-        with gr.TabItem("📝 About", elem_id="qa-tab-table2", id=2):
-            gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
+        # # Table 2
+        # with gr.TabItem("📝 About", elem_id="qa-tab-table2", id=2):
+        #     gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
         
 
 block.launch(share=True)

utils.py

@@ -28,57 +28,19 @@ COLUMN_NAMES = MODEL_INFO
 
 LEADERBORAD_INTRODUCTION = """# VideoScore Leaderboard
 
-    🏆 Welcome to the **VideoScore Leaderboard**! The leaderboard covers many popular text-to-video generative models and evaluates them on 4 dimensions: <br> 
+    🏆 Welcome to the **VideoScore Leaderboard**! The leaderboard covers many popular text-to-video generative models and evaluates them on 5 dimensions: <br> 
     
-    "Visual Quality", "Temporal Consistency", "Dynamic Degree", "Text-to-Video Alignment".
-    
-    To demonstrate the performance of our VideoScore, 
-    we use VideoScore to choose the best from videos with same prompt but different seeds. 
-    Then we use some feature-based metrics mentioned in both <a href="https://arxiv.org/abs/2406.15252">VideoScore paper</a> 
-    and <a href="https://arxiv.org/abs/2310.11440">EvalCrafter paper</a>, 
-    see more info about these metrics in the second sheet "About" above. 
+    "Visual Quality", "Temporal Consistency", "Dynamic Degree", "Text-to-Video Alignment", "Factual Consistency"
     
+    We sample 200 prompts from <a href="https://arxiv.org/abs/2403.06098">VidProM</a> to generate 200 videos using various T2V models (for those closed-source model, we generate 100).    
 
-    <a href='https://hits.seeyoufarm.com'><img src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fhexuan21%2FVideoScore-Leaderboard&count_bg=%23C7C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false'></a>
+    <a href='https://hits.seeyoufarm.com'><img src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2FTIGER-Lab%2FVideoScore-Leaderboard&count_bg=%23C7C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false'></a>
     """
 
 TABLE_INTRODUCTION = """
     """
 
 LEADERBORAD_INFO = """
-Here is the detailed information for the used metrics. <br>
-
-<a href="https://arxiv.org/abs/2406.15252">VideoScore</a> and <a href="https://arxiv.org/abs/2310.11440">EvalCrafter</a> both
-conduct studies about the correlation between these feature-based metrics (like CLIP-Score and SSIM) and the human scoring on generated videos.
-Some of these metrics show a relatively good correlation but some correlates bad with human scores. <br>
-
-Below are the metrics for each dimension, raw score of these metrics is [0,1] and larger is better if there's no extra explanation, then scaled to [0, 100] <br>
-
-(1) Visual Quality = average(VQA_A, VQA_T) <br>
-
-VQA_A and VQA_T are both from EvalCrafter metrics suite. 
-
-
-(2) Temporal Consistency = average(CLIP_Temp, Face_Consistency_Score, Warping_Error) <br>
-
-CLIP_Temp, Face_Consistency_Score, Warping_Error are all from EvalCrafter metrics suite. 
-
-Warping_Error is "100*(1 - raw_result)" so that larger score indicate better performance.  
-
-
-(3) Dynamic Degree = average(SSIM_dyn, MSE_dyn) <br>
-
-SSIM_dyn and MSE_dyn are both from VideoScore. 
-
-SSIM_dyn is "100*(1-raw_result)" so that larger score indicate better performance.  
-
-MSE_dyn is "100*(1-raw_results/255^2)" since the value range of pixel is 0-255 and the theoretical maximum of MSE is 255*255. 
-
-
-(4) Text-to-Video Alignment = average(CLIP-Score, BLIP-BLEU) <br>
-
-CLIP-Scoreand BLIP-BLEU are both from EvalCrafter metrics suite. 
-
 """
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite the t2v models and the used metrics"