  We consider low-shot counting of arbitrary semantic categories in the image
using only few annotated exemplars (few-shot) or no exemplars (no-shot). The
standard few-shot pipeline follows extraction of appearance queries from
exemplars and matching them with image features to infer the object counts.
Existing methods extract queries by feature pooling which neglects the shape
information (e.g., size and aspect) and leads to a reduced object localization
accuracy and count estimates. We propose a Low-shot Object Counting network
with iterative prototype Adaptation (LOCA). Our main contribution is the new
object prototype extraction module, which iteratively fuses the exemplar shape
and appearance information with image features. The module is easily adapted to
zero-shot scenarios, enabling LOCA to cover the entire spectrum of low-shot
counting problems. LOCA outperforms all recent state-of-the-art methods on
FSC147 benchmark by 20-30% in RMSE on one-shot and few-shot and achieves
state-of-the-art on zero-shot scenarios, while demonstrating better
generalization capabilities.
