Abstract
Most object detection frameworks use backbone architectures originally designed for image classification, conventionally with pre-trained parameters on ImageNet. However, image classification and object detection are essentially different tasks and there is no guarantee that the optimal backbone for classification is also optimal for object detection. Recent neural architecture search (NAS) research has demonstrated that automatically designing a backbone specifically for object detection helps improve the overall accuracy. In this paper, we introduce a neural architecture adaptation method that can optimize the given backbone for detection purposes, while still allowing the use of pre-trained parameters. We propose to adapt both the micro- and macro-architecture by searching for specific operations and the number of layers, in addition to the output channel dimensions of each block. It is important to find the optimal channel depth, as it greatly affects the feature representation capability and computation cost. We conduct experiments with our searched backbone for object detection and demonstrate that our backbone outperforms both manually designed and searched state-of-the-art backbones on the COCO dataset.
Original language | English |
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Title of host publication | 2022 26th International Conference on Pattern Recognition, ICPR 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2393-2400 |
Number of pages | 8 |
ISBN (Electronic) | 9781665490627 |
DOIs | |
Publication status | Published - 2022 |
Event | 26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada Duration: 2022 Aug 21 → 2022 Aug 25 |
Publication series
Name | Proceedings - International Conference on Pattern Recognition |
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Volume | 2022-August |
ISSN (Print) | 1051-4651 |
Conference
Conference | 26th International Conference on Pattern Recognition, ICPR 2022 |
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Country/Territory | Canada |
City | Montreal |
Period | 22/8/21 → 22/8/25 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition