Update README.md

README.md

@@ -96,40 +96,31 @@ configs:
       path: visual_prompting_pairs/visual_prompting_val.parquet
 ---
 
-Motivation: A key question for understanding multimodal performance is analyzing the ability for a model to have basic vs. detailed understanding of images. These capabilities are needed for models to be used in
+A key question for understanding multimodal performance is analyzing the ability for a model to have basic 
+vs. detailed understanding of images. These capabilities are needed for models to be used in
 real-world tasks, such as an assistant in the physical world. While there are many dataset for object detection
 and recognition, there are few that test spatial reasoning and other more targeted task such as visual prompting.
 The datasets that do exist are static and publicly available, thus there is concern that current AI models could
 be trained on these datasets, which makes evaluation with them unreliable. Thus we created a dataset that is
 procedurally generated and synthetic, and tests spatial reasoning, visual prompting, as well as object recognition
-and detection [91] . The datasets are challenging for most AI models and by being procedurally generated the
-16
+and detection. The datasets are challenging for most AI models and by being procedurally generated the
 benchmark can be regenerated ad infinitum to create new test sets to combat the effects of models being trained
 on this data and the results being due to memorization.
-Benchmark Description: This dataset has 4 sub-tasks: Object Recognition, Visual Prompting. Spatial Rea-
-soning, and Object Detection. For each sub-task, the images consist of images of pasted objects on random
-images. The objects are from the COCO [62] object list and are gathered from internet data. Each object is
-masked using the DeepLabV3 object detection model [22] and then pasted on a random background from the
-Places365 dataset [132]. The objects are pasted in one of four locations, top, left, bottom, and right, with small
+
+This dataset has 4 sub-tasks: Object Recognition, Visual Prompting. Spatial Rea-
+soning, and Object Detection. 
+
+For each sub-task, the images consist of images of pasted objects on random
+images. The objects are from the COCO object list and are gathered from internet data. Each object is
+masked using the DeepLabV3 object detection model and then pasted on a random background from the
+Places365 dataset.  The objects are pasted in one of four locations, top, left, bottom, and right, with small
 amounts of random rotation, positional jitter, and scale.
+
 There are 2 conditions “ single” and “ pairs”, for images with one and two objects. Each test set uses 20
 sets of object classes (either 20 single objects or 20 pairs of objects), with four potential locations and four
 backgrounds classes, and we sample 4 instances of object and background. This results in 1280 images per
 condition and sub-task.
 
-What are the experimental design setup dimensions 
-(e.g. settings, prompt templates, dataset subsets) for this benchmark? 
-
-This dataset has 4 variations that test:
-
-- Object Detection
-- Object Recognition
-- Spatial Reasoning
-- Visual Prompting
-
-For each varitions, the images consist of images of pasted objects on random images.
-For each there are 2 conditions "single" and "pairs" each test set has 1280 images and text pairs
-
 __Object Detection__
 
 	Answer type: Open-ended