Synthetic Instance Segmentation from Semantic Image Segmentation Masks

• URL: http://arxiv.org/abs/2308.00949v4
• Date: Tue, 08 Oct 2024 02:06:04 GMT
• Title: Synthetic Instance Segmentation from Semantic Image Segmentation Masks
• Authors: Yuchen Shen, Dong Zhang, Zhao Zhang, Liyong Fu, Qiaolin Ye,
• Abstract summary: We propose a novel paradigm called Synthetic Instance (SISeg) SISeg instance segmentation results by leveraging image masks generated by existing semantic segmentation models. In other words, the proposed model does not need extra manpower or higher computational expenses.
• Score: 15.477053085267404
• License:
• Abstract: In recent years, instance segmentation has garnered significant attention across various applications. However, training a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods, such as those using image-level class labels or point labels, often struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm called Synthetic Instance Segmentation (SISeg). SISeg achieves instance segmentation results by leveraging image masks generated by existing semantic segmentation models, and it is highly efficient as we do not require additional training for semantic segmentation or the use of instance-level image annotations. In other words, the proposed model does not need extra manpower or higher computational expenses. Specifically, we first obtain a semantic segmentation mask of the input image via an existent semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, the instance segmentation results are refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets highlight the effectiveness of SISeg in achieving competitive results when compared to state-of-the-art methods, especially fully-supervised methods. The code will be released at: SISeg

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