  Existing object localization methods are tailored to locate specific classes
of objects, relying heavily on abundant labeled data for model optimization.
However, acquiring large amounts of labeled data is challenging in many
real-world scenarios, significantly limiting the broader application of
localization models. To bridge this research gap, this paper defines a novel
task named Few-Shot Object Localization (FSOL), which aims to achieve precise
localization with limited samples. 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 field, we design an innovative high-performance baseline model. This 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 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. All codes and data are available at https://github.com/Ryh1218/FSOL.
