标题： FS-SAM2：通过低秩适应将分割任何模型2用于少样本语义分割 显示英文标题

标题： FS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation

摘要： 少量样本语义分割最近引起了广泛关注。 目标是开发一种能够使用仅有的少量标注样本对未见类别进行分割的模型。 大多数现有方法通过从头开始训练额外模块来适应预训练模型。 这些方法要实现最佳性能需要在大规模数据集上进行大量训练。 Segment Anything Model 2 (SAM2) 是一个用于零样本图像和视频分割的基础模型，具有模块化设计。 在本文中，我们提出了一种基于 SAM2 的少量样本分割方法 (FS-SAM2)，其中直接重新利用 SAM2 的视频功能来进行少量样本任务。 此外，我们对原始模块应用了低秩适应 (LoRA)，以处理标准数据集中通常存在的多样化图像，这与 SAM2 预训练中使用的时序连接帧不同。 通过这种方法，仅需元训练少量参数，就能有效适应 SAM2，同时受益于其出色的分割性能。 我们的方法支持任何 K 次配置。 我们在 PASCAL-5$^i$、COCO-20$^i$和 FSS-1000 数据集上评估了 FS-SAM2，取得了显著的结果，并在推理过程中展示了卓越的计算效率。 代码可在 https://github.com/fornib/FS-SAM2 获取。 显示英文摘要

摘要： Few-shot semantic segmentation has recently attracted great attention. The goal is to develop a model capable of segmenting unseen classes using only a few annotated samples. Most existing approaches adapt a pre-trained model by training from scratch an additional module. Achieving optimal performance with these approaches requires extensive training on large-scale datasets. The Segment Anything Model 2 (SAM2) is a foundational model for zero-shot image and video segmentation with a modular design. In this paper, we propose a Few-Shot segmentation method based on SAM2 (FS-SAM2), where SAM2's video capabilities are directly repurposed for the few-shot task. Moreover, we apply a Low-Rank Adaptation (LoRA) to the original modules in order to handle the diverse images typically found in standard datasets, unlike the temporally connected frames used in SAM2's pre-training. With this approach, only a small number of parameters is meta-trained, which effectively adapts SAM2 while benefiting from its impressive segmentation performance. Our method supports any K-shot configuration. We evaluate FS-SAM2 on the PASCAL-5$^i$, COCO-20$^i$ and FSS-1000 datasets, achieving remarkable results and demonstrating excellent computational efficiency during inference. Code is available at https://github.com/fornib/FS-SAM2