PMR_dataset
URL for Access
Dataset can be downloaded at Google Drive.
Data Format
{
"total_id": 98,
# Name of movie which the image is from.
"movie": "3051_NANNY_MCPHEE_RETURNS",
# Object tags from Fast RCNN
"objects": ["person", "person", "handbag", "spoon"],
# Path of the image
"img_fn": "lsmdc_3051_NANNY_MCPHEE_RETURNS/3051_NANNY_MCPHEE_RETURNS_01.19.27.912-01.19.30.662@0.jpg",
# Id of the image
"img_id": "train-5244",
# Path of the file storing the information of bounding boxes
"metadata_fn": "lsmdc_3051_NANNY_MCPHEE_RETURNS/3051_NANNY_MCPHEE_RETURNS_01.19.27.912-01.19.30.662@0.json",
# Tokenized premise, the integers in lists indicate the index of objects in the above list.
"premise": [[1], "and", [0], "are", "in", "good", "relationship", "."],
# Category of the premise.
"category": "character"
# Tokenized actions, the intergers in lists indicate the index of objects.
"answer_choices": [
[[1], "with", "a", "handbag", "will", "hug", [0], "tightly", "."],
[[1], "with", "a", "green", "handbag", "will", "shout", "at", [0], "in", "the", "kitchen", "."],
[[1], "with", "a", "handbag", "will", "shout", "at", [0], "in", "the", "kitchen", "."],
[[1], "with", "a", "green", "handbag", "will", "hug", [0], "tightly", "."]
],
# The types of answers in the order corresponding to the answer_choices
"answer_types": ["Action-True", "Distractor2", "Action-False", "Distractor1"],
# The index of the correct answer in answer_choices.
"answer_label": 0
# For original set, the total_id of the sample that has the same image as the current sample if it exists.(-1 is the default)
"pal_id":-1
# For adversarial set, the list of total_id which the four choices are from.
"answer_ori_ids":[14097, 12681, 387, 13170]
}