用模板匹配与回归实现少样本模式检测，能精准定位非物体类模式。
Few-Shot Pattern Detection via Template Matching and Regression
- 基于模板匹配与回归，保留示例的空间结构信息。
- 在三个基准上超越现有方法，跨数据集泛化能力强。
- 新数据集RPINE涵盖更广模式类型，适合研究通用模式识别。
我们解决少样本模式检测问题，即从输入图像中检测给定模式的所有实例，通常由少量示例表示。尽管类似问题已在少样本物体计数与检测（FSCD）中被研究，但以往方法及其基准多局限于物体类别，难以定位非物体模式。本文提出一种基于模板匹配与回归的简单而有效的方法TMR。不同于以往方法将目标示例压缩为空间聚合原型而丢失结构信息，我们重新审视经典模板匹配与回归方法，在冻结主干网络基础上仅添加少量可学习卷积或投影层，最小化结构设计却有效保留示例的空间布局。同时引入新数据集RPINE，覆盖比现有以物体为中心的数据集更广泛的模式。所提方法在三个基准（RPINE、FSCD-147、FSCD-LVIS）上均优于当前最优方法，并在跨数据集评估中展现出强泛化能力。
原文摘要 · Abstract (English)
We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchmarks have narrowed patterns of interest to object categories and often fail to localize non-object patterns. In this work, we propose a simple yet effective detector based on template matching and regression, dubbed TMR. While previous FSCD methods typically represent target exemplars as spatially collapsed prototypes and lose structural information, we revisit classic template matching and regression. It effectively preserves and leverages the spatial layout of exemplars through a minimalistic structure with a small number of learnable convolutional or projection layers on top of a frozen backbone We also introduce a new dataset, dubbed RPINE, which covers a wider range of patterns than existing object-centric datasets. Our method outperforms the state-of-the-art methods on the three benchmarks, RPINE, FSCD-147, and FSCD-LVIS, and demonstrates strong generalization in cross-dataset evaluation.
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。