HICO & HICO-DET
Benchmarks for Recognizing Human-Object Interactions in Images
Introduction
We introduce two new benchmarks for classifying and detecting human-object interactions (HOI) in images:
- HICO (Humans Interacting with Common Objects)
- HICO-DET
Key features:
- A diverse set of interactions with common object categories
- A list of well-defined, sense-based HOI categories
- An exhaustive labeling of co-occurring interactions with an object category in each image
- The annotation of each HOI instance (i.e. a human and an object bounding box with an interaction class label) in all images
Tasks
Task 1: HOI Classification
The input is an image and the output is a set of binary labels, each representing the presence or absense of an HOI class.
Sample annotations in the HICO benchmark
Task 2: HOI Detection
The input is an image and the output is a set of bounding box pairs, each localizes a human plus an object and predicts an HOI class label.
Paper
Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng.
HICO: A Benchmark for Recognizing Human-Object Interactions in Images.
IEEE International Conference on Computer Vision ( ICCV), 2015.
[ pdf] [ supplementary material] [ poster] [ bibtex]
Yu-Wei Chao, Yunfan Liu, Xieyang Liu, Huayi Zeng, and Jia Deng.
Learning to Detect Human-Object Interactions.
IEEE Winter Conference on Applications of Computer Vision ( WACV), 2018.
[ pdf] [ supplementary material] [ arXiv] [ poster] [ bibtex]
Dataset
Note:
- HICO and HICO-DET share the same set of HOI categories.
- HICO-DET is a superset of HICO in terms of the image set.
Source Code
hico_benchmark
Source code for reproducing the empirical results in the ICCV 2015 paper.
ho-rcnn
Source code for reproducing the empirical results in the WACV 2018 paper.
Video Spotlight
People
Contact
Send any comments or questions to Yu-Wei Chao: ywchao@umich.edu.
Last updated on 2018/03/08
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