English(EN) MESAM: Enhancing SAM2 for Few-Shot Segmentation through Semantic-Visual Memory Generation and Refinement

MESAM通过记忆生成增强SAM2的少样本分割能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 05:00

研究人员开发了MESAM，一种旨在增强SAM2模型在少样本分割任务中能力的新方法。MESAM通过生成和精炼语义视觉记忆来解决支持图像和查询图像之间外观差异的挑战。该方法包含一个语义视觉记忆生成模块，为查询语义、查询前景视觉和支持视觉创建不同的记忆，随后通过一个交互式语义记忆精炼模块，用视觉细节丰富语义记忆并减少背景噪声。在标准数据集上的实验表明，MESAM在少样本分割方面取得了最先进的成果。 AI

影响提高了少样本分割能力，可能增强AI在仅用少量示例识别物体方面的能力。

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arXiv cs.CV TIER_1 English(EN) · Tianyu Zou, Shengwu Xiong, Rui Wu, Ruilin Yao, Yi Rong · 2026-11-09 05:00

MESAM：通过语义视觉记忆生成与精炼增强SAM2以实现少样本分割

arXiv:2611.08943v1 Announce Type: new Abstract: This paper investigates how to leverage the powerful capabilities of the Segment Anything Model 2 (SAM2) to tackle few-shot segmentation (FSS) tasks. One major challenge lies in the substantial appearance differences between the sup…

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MESAM：通过语义视觉记忆生成与精炼增强SAM2以实现少样本分割

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