SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation

• URL: http://arxiv.org/abs/2303.08578v1
• Date: Tue, 14 Mar 2023 05:59:25 GMT
• Title: SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
• Authors: Ruihuang Li, Chenhang He, Yabin Zhang, Shuai Li, Liyi Chen, Lei Zhang
• Abstract summary: We propose a new box-supervised instance segmentation approach by developing a Semantic-aware Instance Mask (SIM) generation paradigm. Considering that the semantic-aware prototypes cannot distinguish different instances of the same semantics, we propose a self-correction mechanism. Extensive experimental results demonstrate the superiority of our proposed SIM approach over other state-of-the-art methods.
• Score: 22.930296667684125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without explicitly exploiting the high-level semantic information of the objects, which will become ineffective when the foreground objects have similar appearances to the background or other objects nearby. We propose a new box-supervised instance segmentation approach by developing a Semantic-aware Instance Mask (SIM) generation paradigm. Instead of heavily relying on local pair-wise affinities among neighboring pixels, we construct a group of category-wise feature centroids as prototypes to identify foreground objects and assign them semantic-level pseudo labels. Considering that the semantic-aware prototypes cannot distinguish different instances of the same semantics, we propose a self-correction mechanism to rectify the falsely activated regions while enhancing the correct ones. Furthermore, to handle the occlusions between objects, we tailor the Copy-Paste operation for the weakly-supervised instance segmentation task to augment challenging training data. Extensive experimental results demonstrate the superiority of our proposed SIM approach over other state-of-the-art methods. The source code: https://github.com/lslrh/SIM.

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