MARS：一种用于少样本分割的多模态对齐与排序系统
MARS: a Multimodal Alignment and Ranking System for Few-Shot Segmentation
AI总结：
本文提出即插即用的多模态排序系统MARS，通过局部与全局多模态评分过滤合并掩码提议，在多个少样本分割基准上取得新SOTA。
摘要：
少样本分割旨在仅给定少量带标签示例的情况下分割新物体类别，从而在最小监督下实现快速适应。现有文献严重缺乏一种超越查询图像与示例图像之间视觉相似性的选择方法，导致预测结果次优。我们提出MARS，一个即插即用的排序系统，利用多模态线索稳健地过滤和合并掩码提议。从单个查询图像的一组掩码预测出发，我们对它们进行评分、过滤和合并以改进结果。提议通过局部和全局层面计算的多模态分数进行评估。在COCO-20i、Pascal-5i、LVIS-92i和FSS-1000上的大量实验表明，整合全部四个评分组件对于稳健排序至关重要，验证了我们的贡献。由于MARS可以轻松集成到各种掩码提议系统中，我们将其部署到广泛的顶尖方法中，并在多个现有基准上取得了新的最先进结果。代码将在录用后公开。
英文摘要：
Few Shot Segmentation aims to segment novel object classes given only a handful of labeled examples, enabling rapid adaptation with minimal supervision. Current literature crucially lacks a selection method that goes beyond visual similarity between the query and example images, leading to suboptimal predictions. We present MARS, a plug-and-play ranking system that leverages multimodal cues to filter and merge mask proposals robustly. Starting from a set of mask predictions for a single query image, we score, filter, and merge them to improve results. Proposals are evaluated using multimodal scores computed at local and global levels. Extensive experiments on COCO-20i, Pascal-5i, LVIS-92i, and FSS-1000 demonstrate that integrating all four scoring components is crucial for robust ranking, validating our contribution. As MARS can be effortlessly integrated with various mask proposal systems, we deploy it across a wide range of top-performer methods and achieve new state-of-the-art results on multiple existing benchmarks. Code will be available upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
