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- W4385804185 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." @default.
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- W4385804185 date "2024-01-01" @default.
- W4385804185 modified "2023-10-17" @default.
- W4385804185 title "ENInst: Enhancing weakly-supervised low-shot instance segmentation" @default.
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- W4385804185 doi "https://doi.org/10.1016/j.patcog.2023.109888" @default.
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