Update README.md

README.md

@@ -111,15 +111,15 @@ We find that compared to the baseline model SD 2.1, we largely improve the spati
 
 The following table compares our SPRIGHT-T2I model with SD 2.1 across multiple spatial reasoning and image quality:
 
-|Method            |OA(%) ↑|VISOR-4(%) ↑|T2I-CompBench ↑|FID ↓|CCMD ↓|
+|Method            |OA(%) ↑|VISOR-4(%) ↑|T2I-CompBench ↑|FID ↓|CMMD ↓|
 |------------------|-------|------------|---------------|-----|------|
 |SD v2.1           |47.83  |4.70        |0.1507         |21.646|1.060 |
 |SPRIGHT-T2I (ours)|60.68  |16.15       |0.2133         |16.149|0.512 |  
  
 Our key findings are: 
- - Increased the Object Accuracy (OA) score by 26.86%, indicating that we are much better at generating objects mentioned in the input prompt
- - Visor-4 score of 16.15% denotes that for a given input prompt, we consistently generate a spatially accurate image
- - Improve on all aspects of the VISOR score while improving the ZS-FID and CMMD score on COCO-30K images by 23.74% and 51.69%, respectively
+ - We increase the VISOR Object Accuracy (OA) score by 26.86%, indicating that we are much better at generating objects mentioned in the input prompt.
+ - VISOR-4 score of 16.15% denotes that for a given input prompt, we consistently generate a spatially accurate image.
+ - Improve on all aspects of the VISOR score while improving the ZS-FID and CMMD score on COCO-30K images by 23.74% and 51.69%, respectively.
  - Enhance the ability to generate 1 and 2 objects, along with generating the correct number of objects, as indicated by evaluation on the [GenEval](https://github.com/djghosh13/geneval) benchmark.
 
 ### Model Resources