Update README.md

README.md

@@ -21,14 +21,15 @@ Project page: https://dgominski.github.io/drift/
 
 GitHub page: https://github.com/sizhuoli/Domain_adaptive_regression_with_ordered_embedding_space
 
-Publication: ECCV 2024 proceeding: Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring (https://arxiv.org/abs/2405.00514)
+Publication: **ECCV 2024 proceeding**: Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring (https://arxiv.org/abs/2405.00514)
 
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 ## Description
 
-The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. 
-Each image patch is associated with three target variables to predict:
+The DRIFT dataset includes **25k** image patches collected in five European countries sourced from aerial and nanosatellite image archives. 
+
+Each image patch is associated with **3** target variables to predict:
 
 1. Canopy height: average height value for pixels containing woody vegetation.
 
@@ -38,6 +39,7 @@ Each image patch is associated with three target variables to predict:
 
 
 The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. 
+
 Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries).