We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.
@article{arxiv.2302.09765,
title = {ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation},
author = {Moon Ye-Bin and Dongmin Choi and Yongjin Kwon and Junsik Kim and Tae-Hyun Oh},
journal= {arXiv preprint arXiv:2302.09765},
year = {2023}
}