Learning to Detect Human-Object Interactions
Paper • 1702.05448 • Published
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End of preview. Expand in Data Studio
HICO-DET (COCO format)
The HICO-DET human-object interaction benchmark
(Chao et al., WACV 2018) as a scene-graph dataset in
COCO format: object boxes, and each annotated interaction as a directed
(person, predicate, object) relation. It is the HICO-DET data
RelateAnything trains and evaluates on (the
hicodet pack of maelic/OV-SGG-Bench),
in the layout of maelic/PSG-coco-format
and maelic/VG150-coco-format.
Statistics
| Split | Images | Object annotations | Relations |
|---|---|---|---|
| train | 37 633 | 86 799 | 64 929 |
| test | 9 546 | 22 843 | 18 846 |
Layout
data/train-*.parquet, data/test-*.parquet image, image_id, width, height, file_name, objects, relations
annotations/{train,test}.json the same annotations as COCO-SGG json
categories.json 80 object categories, 116 predicates
Boxes are COCO [x, y, width, height] in pixels. A relation's subject_id and object_id
are the ids of the image's objects; predicate_id indexes rel_categories. Both splits
share the category ids (the 80 COCO classes in COCO order, ids 1-80, with HICO-DET's
spellings such as dining_table) and the predicate ids (alphabetical, 0-115).
from datasets import load_dataset
ds = load_dataset("maelic/HICO-DET-coco-format", split="test")
row = ds[0]
row["image"], row["objects"][0]["bbox"], row["relations"][0]
From HICO-DET to scene graphs
- Each annotated interaction becomes one relation from the person box to the object box.
The predicate is the verb's gerund (
ride->riding,sit_on->sitting on): the 116 verbs of HICO-DET withoutno_interaction. - HICO-DET stores one person box and one object box per interaction, so the same object appears several times with slightly different coordinates. Boxes of the same category that overlap at IoU >= 0.5 (HICO-DET's own matching criterion) are merged into one object (their mean box); a relation whose two ends merge is dropped.
- Invisible instances are skipped, and an image keeps at most 40 boxes.
no_interactionannotations and the image-level negative captions are not relations here; RelateAnything turns them into the explicit negatives ofmaelic/OV-SGG-Bench(datamix/hicodet_negatives*.json).
The conversion is
training/convert_hicodet.py;
this repository is built by
release/build_hicodet_coco.py.
Predicates (116)
adjusting · assembling · blocking · blowing · boarding · breaking · brushing with · buying · carrying · catching · chasing · checking · cleaning · controling · cooking · cutting · cutting with · directing · dragging · dribbling · drinking with · driving · drying · eating · eating at · exiting · feeding · filling · flipping · flushing · flying · greeting · grinding · grooming · herding · hitting · holding · hopping on · hosing · hugging · hunting · inspecting · installing · jumping · kicking · kissing · lassoing · launching · licking · lifting · lighting · loading · losing · lying on · making · milking · moving · opening · operating · packing · painting · parking · paying · peeling · petting · picking · picking up · pointing · pouring · pulling · pushing · racing · reading · releasing · repairing · riding · rowing · running · sailing · scratching · serving · setting · shearing · signing · sipping · sitting at · sitting on · sliding · smelling · spining · squeezing · stabbing · standing on · standing under · sticking · stirring · stopping at · straddling · swinging · tagging · talking on · teaching · texting on · throwing · toasting · training · turning · tying · typing on · walking · washing · watching · waving · wearing · wielding · zipping
Images and license
The images are those of HICO-DET as distributed in
zhimeng/hico_det; their JPEG bytes
are copied unchanged. Images and annotations remain under the terms of the original
HICO-DET release (project page); use them
for research and cite the original work:
@inproceedings{chao2018learning,
title = {Learning to Detect Human-Object Interactions},
author = {Chao, Yu-Wei and Liu, Yunfan and Liu, Xieyang and Zeng, Huayi and Deng, Jia},
booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
year = {2018}
}
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