Existing few-shot object counting tasks primarily focus on quantifying the
number of objects in an image, neglecting precise positional information. To
bridge this research gap, this paper introduces the novel task of Few-Shot
Object Localization (FSOL), which aims to provide accurate object positional
information. This task achieves generalized object localization by leveraging a
small number of labeled support samples to query the positional information of
objects within corresponding images. To advance this research field, we propose
an innovative high-performance baseline model. Our model integrates a dual-path
feature augmentation module to enhance shape association and gradient
differences between supports and query images, alongside a self-query module
designed to explore the association between feature maps and query images.
Experimental results demonstrate a significant performance improvement of our
approach in the FSOL task, establishing an efficient benchmark for further
research.

该论文介绍了少样本目标定位 (Few-Shot Object Localization, FSOL) 这一新兴任务，旨在提供精确的目标位置信息。作者提出了一种创新的高性能基准模型，该模型通过整合双路径特征增强模块和自查询模块来增强形状关联和梯度差异，并在 FSOL 任务中取得了显著的性能提升，为进一步研究建立了高效的基准。