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video_id
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stringlengths 43
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755
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sequence |
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v_QOlSCBRmfWY | https://www.youtube.com/watch?v=QOlSCBRmfWY | 82.730003 | [
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"A young woman is seen standing in a room and leads into her dancing.",
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" She continues dancing around the room and ends by laying on the floor."
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v_ehGHCYKzyZ8 | https://www.youtube.com/watch?v=ehGHCYKzyZ8 | 61.720001 | [
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"The video starts with a title logo sequence.",
" A man and woman are in a living room demonstrating exercises.",
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" The woman begins to do small sit ups.",
" The woman ends with a final title logo sequence."
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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.",
" A dog in socks walks slowly out onto the floor as a lady films him.",
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v_nHE7u40plD0 | https://www.youtube.com/watch?v=nHE7u40plD0 | 145.559998 | [
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" A little boy holding a yellow ball walks by.",
" A man poses for a picture in front of them."
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v_69IsHpmRyfk | https://www.youtube.com/watch?v=69IsHpmRyfk | 94.720001 | [
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"A young girl is seen sitting in a chair with a person standing next to her.",
" The person next to her then piercing one ear followed by the other.",
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v_D18b2IZpxk0 | https://www.youtube.com/watch?v=D18b2IZpxk0 | 95.669998 | [
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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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" She lays out wrapping paper, showing how to wrap a toy in it.",
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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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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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"The man is talking this whole time while the woman gets comfortable on her couch and the woman briefly talks.",
"Various clips begin to play and it briefly shows parts of a city, then most of it is of men outdoors and playing soccer on sand in a middle of city, and occasionally blue words pop up on the screen when certain plays in the game are made.",
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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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"A man is playing the bagpipes in front of people.",
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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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"A group of people stand in front of a blackjack table.",
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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_fN2DiOswmOA | https://www.youtube.com/watch?v=fN2DiOswmOA | 185.479996 | [
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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_74EoyLUbtgI | https://www.youtube.com/watch?v=74EoyLUbtgI | 171.220001 | [
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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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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
:str
unique identifier for the videovideo_path
:str
Path to the video file -duration
:float32
Duration of the videocaptions_starts
:List_float32
List of timestamps denoting the time at which each caption startscaptions_ends
:List_float32
List of timestamps denoting the time at which each caption endsen_captions
:list_str
List 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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