Update README.md

README.md

@@ -6,21 +6,27 @@ Here we create two datasets (from existing datasets: CLEVRER, VisualGenome) for
 
 ### CLEVRER
 
-CLEVRER has QA pairs for each 5000 training video 
+CLEVRER has QA pairs for each 5000 training videos.
 ```json
 {'video_filename': int, 'scene_index': str (same as filename), 'questions': list [{'question_type': , 'question_subtype': , 'question_text': , 'answer_text': , 'program'(question attributes): }]} 
 ```
 We select 'descriptive' type, 'count' subtype questions, they are object counting. 
+
 CLEVRER contains both positive questions and negative (non-exist) questions, so no need to construct negative samples.
+
 some questions are 'event' specific, counting moving/stationary objects when a certain event happens. i.e., 'How many objects are stationary when the yellow object enters the scene?'
-downloading videos: http://clevrer.csail.mit.edu/
+
+downloading videos from: http://clevrer.csail.mit.edu/
 
 
 ### VisualGenome
 
 We generate some negative questions for non-exist objects in the image. We use the version 1 image sets. Download from: https://homes.cs.washington.edu/~ranjay/visualgenome/api.html
+
 VisualGenome has 100K+ images. And for the objects in the image, there are attributes, We only focus on the color attributes.
-There are in total 11K+ possible objects. for each image, I add 3 non-exist objects and 1 non-exist attribute for existing objects as negative samples. 
+
+There are in total 11K+ possible objects. for each image, I add 3 non-exist objects and 1 non-exist attribute for existing objects as negative samples.
+
 In the original qa dataset, VG has Object Counting questions, we also include them here, with the 'orig_qa'=='Yes'. For those negative questions we generated, 'orig_qa' =='No'.
 ```json
 {'img_id': str, 'orig_qa': Yes/No, 'question_text': 'How many <attribute> <object in plural form> are there? ', 'answer_text': Numbers. or None. }