Alternative Solution to Catastrophical Forgetting on FewShot Instance Segmentation

Abstract: Video instance segmentation is a rapidly-growing research area within the computer vision field. Models for segmentation require data already annotated, which can be a daunting task when starting from scratch. Although there are some publicly available datasets for image instance segmentation, they are limited to the application they target. This work proposes a new approach to training an instance segmentation model using transfer learning, notably reducing the need for annotated data. Transferring knowledge from domain A to domain B can result in catastrophical forgetting, leading to an algorithm unable to properly generalize and remember the previous knowledge acquired at the initial domain. This problem is studied and a solution is proposed based on data transformations applied precisely at the process of transferring knowledge to the target domain following the empirical research method and using publicly available video instance segmentation datasets as resources for the experiments. Conclusions show there is a relationship between the data transformations and ability to generalize both domains.