When humans have to solve everyday tasks, they simply pick the objects that are most suitable. While the question which object should one use for a specific task sounds trivial for humans, it is very difficult to answer for robots or other autonomous systems. This issue, however, is not addressed by current benchmarks for object detection that focus on detecting object categories. We therefore introduce the COCO-Tasks dataset which comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated. We furthermore propose an approach that detects the most suitable objects for a given task. The approach builds on a Gated Graph Neural Network to exploit the appearance of each object as well as the global context of all present objects in the scene. In our experiments, we show that the proposed approach outperforms other approaches that are evaluated on the dataset like classification or ranking approaches.
@article{arxiv.1904.03000,
title = {What Object Should I Use? - Task Driven Object Detection},
author = {Johann Sawatzky and Yaser Souri and Christian Grund and Juergen Gall},
journal= {arXiv preprint arXiv:1904.03000},
year = {2019}
}
Comments
CVPR 2019. The first two authors contributed equally, ordered alphabetically