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What Object Should I Use? - Task Driven Object Detection
- Source :
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), CVPR
- Publication Year :
- 2019
-
Abstract
- 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.<br />CVPR 2019. The first two authors contributed equally, ordered alphabetically
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Context (language use)
010501 environmental sciences
01 natural sciences
Machine Learning (cs.LG)
Task (project management)
Computer Science - Robotics
0502 economics and business
Computer vision
050207 economics
0105 earth and related environmental sciences
business.industry
05 social sciences
Object (computer science)
Object detection
Categorization
Ranking
Robot
Artificial intelligence
Focus (optics)
business
Robotics (cs.RO)
Subjects
Details
- ISBN :
- 978-1-72813-293-8
- ISBNs :
- 9781728132938
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Accession number :
- edsair.doi.dedup.....a057b3eeab0ccc3d61c95d3b06fafa2e
- Full Text :
- https://doi.org/10.1109/cvpr.2019.00779