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Towards Open World ...
Towards Open World Object Detection
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- Joseph, KJ (author)
- Indian Institute of Technology Hyderabad, India,Indian Inst Technol Hyderabad, India; Mohamed Bin Zayed Univ AI, U Arab Emirates
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- Khan, Salman (author)
- Mohamed bin Zayed University of AI, UAE, Australian National University, Australia,Mohamed Bin Zayed Univ AI, U Arab Emirates; Australian Natl Univ, Australia
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- Khan, Fahad Shahbaz, 1983- (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Mohamed bin Zayed University of AI, UAE,Mohamed Bin Zayed Univ AI, U Arab Emirates
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- Balasubramanian, Vineeth N (author)
- Indian Institute of Technology Hyderabad, India,Indian Inst Technol Hyderabad, India
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(creator_code:org_t)
- IEEE COMPUTER SOC, 2021
- 2021
- English.
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Series: arXiv.org ; 2103.02603
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In: 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021. - : IEEE COMPUTER SOC. - 9781665445092 ; , s. 5826-5836
- Related links:
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https://arxiv.org/ab...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosityabout these unknown instances aids in learning about them,when the corresponding knowledge is eventually available.This motivates us to propose a novel computer vision problem called: ‘Open World Object Detection’, where a modelis tasked to: 1) identify objects that have not been introduced to it as ‘unknown’, without explicit supervision to doso, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, whenthe corresponding labels are progressively received. Weformulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE:Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacyof ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterisingunknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort.We hope that our work will attract further research into thisnewly identified, yet crucial research direction.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publication and Content Type
- vet (subject category)
- kon (subject category)
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