Few-shot Object Localization
arxiv(2024)
摘要
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.
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