Improving Instance Segmentation using Synthetic Data with Artificial Distractors
Kanghyun Park, Hyeongkeun Lee, Hunmin Yang, Se-Yoon Oh · 2020
Despite the advances in deep learning, training instance segmentation models like convolutional neural networks still tend to depend on enormous training data that are expensive and require labor to annotation. To avoid labor-intensive procedure, synthetic data can be an alternative because it is easy to generate and automatically segmented. However, it is challenging to train instance segmentation model that perform well at real world using only synthetic data because of domain gap. It is wrong direction to put a lot of effort into solving these problems by making synthetic data more photorealistic. In this paper, we suggest how to learn the instance segmentation model using synthetic data with artificial distractors. The performance has been improved about 7% by adding flying distractors compared to original synthetic data.