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image
image | image_url
string | dataset_id
string | image_id
string | annotator_id
int32 | caption
string | timed_caption
sequence | traces
sequence | voice_recording
string |
|---|---|---|---|---|---|---|---|---|
open_images | 6375c17e61428d75 | 96 | In this image there are some persons standing at right side of this image and there are some lights are arranged at top of this image. There are some cabins as we can see at left side of this image and there is a floor at bottom of this image. | {
"utterance": [
"In",
"this image there are some persons",
"standing",
"at",
"right",
"side",
"of",
"this",
"image",
"and",
"there are",
"some",
"lights",
"are",
"arranged",
"at",
"top of",
"this",
"image. There are some",
"cabins",
"as",
"we",
"can",
"see",
"at",
"left",
"side of",
"this",
"image",
"and",
"there",
"is",
"a",
"floor",
"at",
"bottom",
"of",
"this",
"image."
],
"start_time": [
0,
1,
1.2,
2.2,
2.4,
2.5,
2.8,
2.8,
3,
3.5,
4.9,
5.3,
5.4,
5.8,
5.9,
6.5,
6.8,
6.8,
7.2,
7.6,
12.9,
13.5,
13.5,
14,
14,
14.4,
14.4,
14.6,
15.2,
15.3,
18.9,
19.1,
19.3,
19.4,
19.7,
20.4,
20.9,
20.9,
21.2
],
"end_time": [
1,
1.2,
2.2,
2.4,
2.5,
2.8,
2.8,
3,
3.5,
4.9,
5.3,
5.4,
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5.9,
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14,
14,
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18.9,
19.1,
19.3,
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20.4,
20.9,
20.9,
21.2,
21.5
]
} | [
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||
open_images | 6d67d89eb7194b30 | 60 | In this image we can see a pie on a large spatula. On the backside we can see some fire. | {
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||
open_images | bbae2763a7fe8f66 | 27 | Here we can see a woman having a football in her hand and behind her we can see group of people standing and it is snowy | {
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||
open_images | 6ae2c810859998c8 | 108 | In this picture, we can see a few buildings, poles, stairs, railing, road, a few people, and vehicles, and the sky with clouds, and we can see date and time in bottom right side of the picture. | {
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open_images | 0b4002ac1436cbfc | 46 | This image is taken in a store where we can see mirrors, chairs, wall, lights, ceiling, bottles, tripod stands, frames, jar, table and a cupboard. | {
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] | open_images_train/open_images_train_0b4002ac1436cbfc_46.ogg |
End of preview.
Dataset Card for [Dataset Name]
Dataset Summary
Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data. We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available.
As of now, there is only the OpenImages subset, but feel free to contribute the other subset of Localized Narratives!
OpenImages_captions is similar to the OpenImages subset. The differences are that captions are groupped per image (images can have multiple captions). For this subset, timed_caption, traces and voice_recording are not available.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
Each instance has the following structure:
{
dataset_id: 'mscoco_val2017',
image_id: '137576',
annotator_id: 93,
caption: 'In this image there are group of cows standing and eating th...',
timed_caption: [{'utterance': 'In this', 'start_time': 0.0, 'end_time': 0.4}, ...],
traces: [[{'x': 0.2086, 'y': -0.0533, 't': 0.022}, ...], ...],
voice_recording: 'coco_val/coco_val_137576_93.ogg'
}
Data Fields
Each line represents one Localized Narrative annotation on one image by one annotator and has the following fields:
dataset_id: String identifying the dataset and split where the image belongs, e.g. mscoco_val2017.image_idString identifier of the image, as specified on each dataset.annotator_idInteger number uniquely identifying each annotator.captionImage caption as a string of characters.timed_captionList of timed utterances, i.e. {utterance, start_time, end_time} where utterance is a word (or group of words) and (start_time, end_time) is the time during which it was spoken, with respect to the start of the recording.tracesList of trace segments, one between each time the mouse pointer enters the image and goes away from it. Each trace segment is represented as a list of timed points, i.e. {x, y, t}, where x and y are the normalized image coordinates (with origin at the top-left corner of the image) and t is the time in seconds since the start of the recording. Please note that the coordinates can go a bit beyond the image, i.e. <0 or >1, as we recorded the mouse traces including a small band around the image.voice_recordingRelative URL path with respect to https://storage.googleapis.com/localized-narratives/voice-recordings where to find the voice recording (in OGG format) for that particular image.
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
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Source Data
Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
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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
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
Thanks to @VictorSanh for adding this dataset.
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Homepage:
google.github.io
Repository:
github.com
Paper:
Connecting Vision and Language with Localized Narratives
Leaderboard:
Leaderboard:
Point of Contact:
Point of Contact:
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