用SAM实现小样本实例分割的增量学习，只需少量标注就能精准识别新物体。
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation
- 引入多类分类器并微调SAM，聚焦特定目标对象分割
- 基于余弦相似度的分类器，仅需少量数据即可适应新类别
- 支持增量更新分类器权重，无需重训解码器，适合持续学习场景
我们提出SAM-IF，一种利用分割一切模型（SAM）实现增量小样本实例分割的新方法。SAM-IF通过引入多类分类器并微调SAM，解决无类别先验的实例分割挑战，使模型能专注于特定目标。为增强小样本学习能力，采用基于余弦相似度的分类器，实现仅用极少标注数据即可高效适应新类别。此外，通过仅更新分类器权重而非重训练解码器，支持增量学习。相比现有方法，该模型在有限标注数据下实现了更具竞争力且更合理的分割性能，尤其适用于需持续识别特定物体的场景。
原文摘要 · Abstract (English)
We propose SAM-IF, a novel method for incremental few-shot instance segmentation leveraging the Segment Anything Model (SAM). SAM-IF addresses the challenges of class-agnostic instance segmentation by introducing a multi-class classifier and fine-tuning SAM to focus on specific target objects. To enhance few-shot learning capabilities, SAM-IF employs a cosine-similarity-based classifier, enabling efficient adaptation to novel classes with minimal data. Additionally, SAM-IF supports incremental learning by updating classifier weights without retraining the decoder. Our method achieves competitive but more reasonable results compared to existing approaches, particularly in scenarios requiring specific object segmentation with limited labeled data.
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