The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation

• URL: http://arxiv.org/abs/2303.15062v1
• Date: Mon, 27 Mar 2023 10:11:22 GMT
• Title: The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation
• Authors: Beomyoung Kim, Joonhyun Jeong, Dongyoon Han, Sung Ju Hwang
• Abstract summary: We introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels. We propose a method for WSSIS that can effectively leverage the budget-friendly point labels as a powerful weak supervision source. We conduct extensive experiments on COCO and BDD100K datasets, and the proposed method achieves promising results comparable to those of the fully-supervised model.
• Score: 61.027468209465354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation. Namely, we consider a dataset setting consisting of a few fully-labeled images and a lot of point-labeled images. Motivated by the main challenge of semi-supervised approaches mainly derives from the trade-off between false-negative and false-positive instance proposals, we propose a method for WSSIS that can effectively leverage the budget-friendly point labels as a powerful weak supervision source to resolve the challenge. Furthermore, to deal with the hard case where the amount of fully-labeled data is extremely limited, we propose a MaskRefineNet that refines noise in rough masks. We conduct extensive experiments on COCO and BDD100K datasets, and the proposed method achieves promising results comparable to those of the fully-supervised model, even with 50% of the fully labeled COCO data (38.8% vs. 39.7%). Moreover, when using as little as 5% of fully labeled COCO data, our method shows significantly superior performance over the state-of-the-art semi-supervised learning method (33.7% vs. 24.9%). The code is available at https://github.com/clovaai/PointWSSIS.

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