Scale-Aware Automatic Augmentations for Object Detection With Dynamic Training

Author:

Chen Yukang1ORCID,Zhang Peizhen2ORCID,Kong Tao3ORCID,Li Yanwei1ORCID,Zhang Xiangyu2ORCID,Qi Lu1ORCID,Sun Jian2,Jia Jiaya1ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong

2. MEGVII Technology, Beijing, China

3. ByteDance AI Lab, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference85 articles.

1. An Analysis of Scale Invariance in Object Detection - SNIP

2. Training Region-Based Object Detectors with Online Hard Example Mining

3. Detectron;girshick,2018

4. Randaugment: Practical automated data augmentation with a reduced search space

5. A Bayesian data augmentation approach for learning deep models;tran;Proc Adv Neural Informat Process Syst,2017

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