ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation

• URL: http://arxiv.org/abs/2302.09765v3
• Date: Mon, 31 Jul 2023 03:05:22 GMT
• Title: ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation
• Authors: Moon Ye-Bin, Dongmin Choi, Yongjin Kwon, Junsik Kim, Tae-Hyun Oh
• Abstract summary: We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. 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.
• Score: 23.621454800084724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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