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:
Tags:
License:
video_id
stringlengths 13
13
| video_path
stringlengths 43
43
| duration
float32 1.58
755
| captions_starts
sequence | captions_ends
sequence | en_captions
sequence |
|---|---|---|---|---|---|
v_QOlSCBRmfWY | https://www.youtube.com/watch?v=QOlSCBRmfWY | 82.730003 | [
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v_ehGHCYKzyZ8 | https://www.youtube.com/watch?v=ehGHCYKzyZ8 | 61.720001 | [
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v_nwznKOuZM7w | https://www.youtube.com/watch?v=nwznKOuZM7w | 31.65 | [
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"Two people are seen moving around a kitchen quickly performing various tasks and sitting down.",
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v_ogQozSI5V8U | https://www.youtube.com/watch?v=ogQozSI5V8U | 36.549999 | [
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"We see a hallway with a wooden floor.",
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v_nHE7u40plD0 | https://www.youtube.com/watch?v=nHE7u40plD0 | 145.559998 | [
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v_69IsHpmRyfk | https://www.youtube.com/watch?v=69IsHpmRyfk | 94.720001 | [
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v_D18b2IZpxk0 | https://www.youtube.com/watch?v=D18b2IZpxk0 | 95.669998 | [
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"A woman is shown riding a camel past pyramids in Egypt.",
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v_pizl41xmw7k | https://www.youtube.com/watch?v=pizl41xmw7k | 172.080002 | [
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v_oP77DgsbhKQ | https://www.youtube.com/watch?v=oP77DgsbhKQ | 214.600006 | [
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v_fzp5ooc727c | https://www.youtube.com/watch?v=fzp5ooc727c | 148.320007 | [
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v_LNKdVrX_0Fg | https://www.youtube.com/watch?v=LNKdVrX_0Fg | 226.720001 | [
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v_t0ajvfx6dgA | https://www.youtube.com/watch?v=t0ajvfx6dgA | 147.259995 | [
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v_IoiDAHNryTk | https://www.youtube.com/watch?v=IoiDAHNryTk | 47.040001 | [
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"A girl is seen climbing across a set of monkey bars followed by her waving to the camera.",
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v__yWADgOFxP0 | https://www.youtube.com/watch?v=_yWADgOFxP0 | 238.380005 | [
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"A camera pans over a snowy area and leads into a man standing on a snowboard and riding down a mountain.",
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v_AGgJVF3nT6I | https://www.youtube.com/watch?v=AGgJVF3nT6I | 65.040001 | [
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"A playroom is seen followed by a young boy sliding down a slide and climbing back up again.",
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v_XD3yFrJHiv8 | https://www.youtube.com/watch?v=XD3yFrJHiv8 | 157.320007 | [
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v_lZKrd84QElk | https://www.youtube.com/watch?v=lZKrd84QElk | 65.830002 | [
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v_6lYTHj9vImo | https://www.youtube.com/watch?v=6lYTHj9vImo | 60.119999 | [
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v_E3QtX6r9QX0 | https://www.youtube.com/watch?v=E3QtX6r9QX0 | 103.370003 | [
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v_iYxj8a1TPYk | https://www.youtube.com/watch?v=iYxj8a1TPYk | 157.410004 | [
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v_cp52LdlmlUk | https://www.youtube.com/watch?v=cp52LdlmlUk | 129.960007 | [
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v_bpg2PC2neQM | https://www.youtube.com/watch?v=bpg2PC2neQM | 218.699997 | [
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v_RgzbNJPchqc | https://www.youtube.com/watch?v=RgzbNJPchqc | 69.239998 | [
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v__zHSfEhEqkU | https://www.youtube.com/watch?v=_zHSfEhEqkU | 194.350006 | [
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"A man is seen padding a canoe along the water while the camera captures him from several angles.",
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v_dAjhBWalWRM | https://www.youtube.com/watch?v=dAjhBWalWRM | 32.09 | [
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v_zihMznAYlV0 | https://www.youtube.com/watch?v=zihMznAYlV0 | 72.800003 | [
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v_---9CpRcKoU | https://www.youtube.com/watch?v=---9CpRcKoU | 14.07 | [
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v_epqnpUCVLK8 | https://www.youtube.com/watch?v=epqnpUCVLK8 | 63.32 | [
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v_GFE2wAi9fjo | https://www.youtube.com/watch?v=GFE2wAi9fjo | 17.15 | [
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v_W19-fYOO9sg | https://www.youtube.com/watch?v=W19-fYOO9sg | 98.150002 | [
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v_m22TDwfa8go | https://www.youtube.com/watch?v=m22TDwfa8go | 183.860001 | [
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v_V_Nj_BhesEY | https://www.youtube.com/watch?v=V_Nj_BhesEY | 83.150002 | [
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v_p9iE8iemCMs | https://www.youtube.com/watch?v=p9iE8iemCMs | 134.580002 | [
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v_37pwbUp8t1I | https://www.youtube.com/watch?v=37pwbUp8t1I | 200.039993 | [
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v_TmI9MxCDBMw | https://www.youtube.com/watch?v=TmI9MxCDBMw | 191.940002 | [
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v_4x7HYKV8zkY | https://www.youtube.com/watch?v=4x7HYKV8zkY | 115.940002 | [
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v__Af_9cK5x4E | https://www.youtube.com/watch?v=_Af_9cK5x4E | 232.289993 | [
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v_y5j9TqTy9Xw | https://www.youtube.com/watch?v=y5j9TqTy9Xw | 44.84 | [
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v_Kp7pUEKrb8Q | https://www.youtube.com/watch?v=Kp7pUEKrb8Q | 89.629997 | [
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v_5lre5zbUV1Q | https://www.youtube.com/watch?v=5lre5zbUV1Q | 77.230003 | [
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v_huFuRGoZt9I | https://www.youtube.com/watch?v=huFuRGoZt9I | 23.360001 | [
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v_CmEQjszPDrg | https://www.youtube.com/watch?v=CmEQjszPDrg | 220.850006 | [
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v_WY9zvUkK_4Y | https://www.youtube.com/watch?v=WY9zvUkK_4Y | 203.589996 | [
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v_L2oaUDyzWPA | https://www.youtube.com/watch?v=L2oaUDyzWPA | 92.089996 | [
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v_fny1HWXezlE | https://www.youtube.com/watch?v=fny1HWXezlE | 144.800003 | [
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v_pYb8jzB5vs0 | https://www.youtube.com/watch?v=pYb8jzB5vs0 | 194.649994 | [
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v_kNkYgYnJTXk | https://www.youtube.com/watch?v=kNkYgYnJTXk | 202.529999 | [
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] |
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YAML Metadata
Warning:
The task_categories "video-captionning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
Dataset Card for ActivityNet Captions
Dataset Summary
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.
Languages
The captions in the dataset are in English.
Dataset Structure
Data Fields
video_id:strunique identifier for the videovideo_path:strPath to the video file -duration:float32Duration of the videocaptions_starts:List_float32List of timestamps denoting the time at which each caption startscaptions_ends:List_float32List of timestamps denoting the time at which each caption endsen_captions:list_strList of english captions describing parts of the video
Data Splits
| train | validation | test | Overall | |
|---|---|---|---|---|
| # of videos | 10,009 | 4,917 | 4,885 | 19,811 |
Annotations
Quoting ActivityNet Captions' paper:
"Each annotation task was divided into two steps: (1)
Writing a paragraph describing all major events happening
in the videos in a paragraph, with each sentence of the paragraph describing one event, and (2) Labeling the
start and end time in the video in which each sentence in the
paragraph event occurred."
Who annotated the dataset?
Amazon Mechnical Turk annotators
Personal and Sensitive Information
Nothing specifically mentioned in the paper.
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Licensing Information
[More Information Needed]
Citation Information
@InProceedings{tgif-cvpr2016,
@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}
}
Contributions
Thanks to @leot13 for adding this dataset.
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