Datasets:
Sub-tasks:
closed-domain-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10k<n<100K
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
import os | |
import json | |
import datasets | |
_CITATION = """ | |
@inproceedings{krishna2017dense, | |
title={Dense-Captioning Events in Videos}, | |
author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos}, | |
booktitle={International Conference on Computer Vision (ICCV)}, | |
year={2017} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions. | |
Each sentence covers an unique segment of the video, describing multiple events that occur. These events | |
may occur over very long or short periods of time and are not limited in any capacity, allowing them to | |
co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in | |
a total of 100k sentences. We find that the number of sentences per video follows a relatively normal | |
distribution. Furthermore, as the video duration increases, the number of sentences also increases. | |
Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more | |
details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials | |
in the paper. | |
""" | |
_URL_BASE = "https://cs.stanford.edu/people/ranjaykrishna/densevid/" | |
class ActivityNetConfig(datasets.BuilderConfig): | |
"""BuilderConfig for ActivityNet Captions.""" | |
def __init__(self, **kwargs): | |
super(ActivityNetConfig, self).__init__( | |
version=datasets.Version("2.1.0", ""), **kwargs) | |
class ActivityNet(datasets.GeneratorBasedBuilder): | |
DEFAULT_CONFIG_NAME = "all" | |
BUILDER_CONFIGS = [ | |
ActivityNetConfig( | |
name="all", description="All the ActivityNet Captions dataset"), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"video_id": datasets.Value("string"), | |
"video_path": datasets.Value("string"), | |
"duration": datasets.Value("float32"), | |
"captions_starts": datasets.features.Sequence(datasets.Value("float32")), | |
"captions_ends": datasets.features.Sequence(datasets.Value("float32")), | |
"en_captions": datasets.features.Sequence(datasets.Value("string")) | |
} | |
), | |
supervised_keys=None, | |
homepage=_URL_BASE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
archive_path = dl_manager.download_and_extract( | |
_URL_BASE + "captions.zip") | |
train_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"infos_file": os.path.join(archive_path, "train.json") | |
}, | |
) | |
] | |
dev_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"infos_file": os.path.join(archive_path, "val_1.json") | |
}, | |
) | |
] | |
test_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"infos_file": os.path.join(archive_path, "val_2.json") | |
}, | |
) | |
] | |
return train_splits + dev_splits + test_splits | |
def _generate_examples(self, infos_file): | |
"""This function returns the examples.""" | |
with open(infos_file, encoding="utf-8") as json_file: | |
infos = json.load(json_file) | |
for idx, id in enumerate(infos): | |
path = "https://www.youtube.com/watch?v=" + id[2:] | |
starts = [float(timestamp[0]) | |
for timestamp in infos[id]["timestamps"]] | |
ends = [float(timestamp[1]) | |
for timestamp in infos[id]["timestamps"]] | |
captions = [str(caption) for caption in infos[id]["sentences"]] | |
yield idx, { | |
"video_id": id, | |
"video_path": path, | |
"duration": float(infos[id]["duration"]), | |
"captions_starts": starts, | |
"captions_ends": ends, | |
"en_captions": captions, | |
} | |