Semantic-aware SAM for Point-Prompted Instance Segmentation
Authors: Zhaoyang Wei, Pengfei Chen, Xuehui Yu, Guorong Li, Jianbin Jiao, Zhenjun Han

Single-point annotation in visual tasks with the goal of minimizing labeling costs is becoming increasingly prominent in research. Recently visual foundation models such as Segment Anything (SAM) have gained widespread usage due to their robust zero-shot capabilities and exceptional annotation performance. However SAM's class-agnostic output and high confidence in local segmentation introduce semantic ambiguity posing a challenge for precise category-specific segmentation. In this paper we introduce a cost-effective category-specific segmenter using SAM. To tackle this challenge we have devised a Semantic-Aware Instance Segmentation Network (SAPNet) that integrates Multiple Instance Learning (MIL) with matching capability and SAM with point prompts. SAPNet strategically selects the most representative mask proposals generated by SAM to supervise segmentation with a specific focus on object category information. Moreover we introduce the Point Distance Guidance and Box Mining Strategy to mitigate inherent challenges: group and local issues in weakly supervised segmentation. These strategies serve to further enhance the overall segmentation performance. The experimental results on Pascal VOC and COCO demonstrate the promising performance of our proposed SAPNet emphasizing its semantic matching capabilities and its potential to advance point-prompted instance segmentation. The code is available at https://github.com/zhaoyangwei123/SAPNet.