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Conference Paper
An Intensive Study of Backbone and Architectures with Test Image Augmentation and Box Refinement for Object Detection and Segmentation
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- Authors
- Keundong Lee, Jong Gook Ko, Wonyoung Yoo
- Issue Date
- 2019-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.673-677
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we present an intensive study of various backbones and architectures with test augmentation and box refinement for object detection and segmentation. Recently, several models discovered by Neural Architecture Search achieve the state-of-the-art in ImageNet Classification. However, their robustness on detection task is not yet verified. While various architectures have been proposed to improve the accuracy, they are rarely evaluated with test-time image augmentation and box refinement which can further boost their gains. In this work, we thoroughly evaluate them on challenging COCO dataset to identify the robustness of backbones on detection task, the effect of various architectures and the state-of-the-art accuracy on object detection and segmentation.
- KSP Keywords
- Detection task, Image Augmentation, Object detection, state-of-The-Art