  The class-agnostic counting (CAC) task has recently been proposed to solve
the problem of counting all objects of an arbitrary class with several
exemplars given in the input image. To address this challenging task, existing
leading methods all resort to density map regression, which renders them
impractical for downstream tasks that require object locations and restricts
their ability to well explore the scale information of exemplars for
supervision. To address the limitations, we propose a novel localization-based
CAC approach, termed Scale-modulated Query and Localization Network (SQLNet).
It fully explores the scales of exemplars in both the query and localization
stages and achieves effective counting by accurately locating each object and
predicting its approximate size. Specifically, during the query stage, rich
discriminative representations of the target class are acquired by the
Hierarchical Exemplars Collaborative Enhancement (HECE) module from the few
exemplars through multi-scale exemplar cooperation with equifrequent size
prompt embedding. These representations are then fed into the Exemplars-Unified
Query Correlation (EUQC) module to interact with the query features in a
unified manner and produce the correlated query tensor. In the localization
stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query
tensor to predict the confidence, location, and size of each potential object.
Moreover, a scale-aware localization loss is introduced, which exploits
flexible location associations and exemplar scales for supervision to optimize
the model performance. Extensive experiments demonstrate that SQLNet
outperforms state-of-the-art methods on popular CAC benchmarks, achieving
excellent performance not only in counting accuracy but also in localization
and bounding box generation. Our codes will be available at
https://github.com/HCPLab-SYSU/SQLNet
