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Reducing the need for bounding box annotations in Object Detection using Image Classification data

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Blanger, Leonardo ; Hirata, Nina S. T. ; Jiang, Xiaoyi ; IEEE Comp Soc
Total Authors: 4
Document type: Journal article
Source: 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01.

We address the problem of training Object Detection models using significantly less bounding box annotated images. For that, we take advantage of cheaper and more abundant image classification data. Our proposal consists in automatically generating artificial detection samples, with no need of expensive detection level supervision, using images with classification labels only. We also detail a pretraining initialization strategy for detection architectures using these artificially synthesized samples, before finetuning on real detection data, and experimentally show how this consistently leads to more data efficient models. With the proposed approach, we were able to effectively use only classification data to improve results on the harder and more supervision hungry object detection problem. We achieve results equivalent to those of the full data scenario using only a small fraction of the original detection data for Face, Bird, and Car detection. (AU)

FAPESP's process: 18/00390-7 - QR code detection using deep learning models
Grantee:Leonardo Blanger
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 17/25835-9 - Understanding images and deep learning models
Grantee:Nina Sumiko Tomita Hirata
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 19/17312-1 - Adversarial learning of image augmentation policies for object detection
Grantee:Leonardo Blanger
Support Opportunities: Scholarships abroad - Research Internship - Master's degree