用多模态信息筛选分割候选框，提升少样本分割准确率
MARS: a Multimodal Alignment and Ranking System for Few-Shot Segmentation
- 通过局部与全局多模态评分筛选候选分割图
- 在四个数据集上均超越现有方法，显著提升分割精度
- 可无缝接入主流分割模型，适合快速部署
少样本分割旨在仅凭少量标注样例即可分割新类别物体，实现低监督下的快速适应。现有方法多依赖查询图像与示例图像的视觉相似性，难以获得最优结果。本文提出MARS——一种即插即用的多模态对齐与排序系统，通过融合多模态线索对分割候选框进行评分、筛选与合并。针对单个查询图像生成的多个掩码预测，系统在局部与全局层面计算多模态得分，优化最终输出。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鲁棒排序至关重要。MARS可轻松集成至多种掩码生成系统，在多个主流基准上实现新的最先进性能。代码将在论文接受后公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。