Query-guided networks for few-shot fine-grained classification and person search

Bharti Munjal, Alessandro Flaborea, Sikandar Amin, Federico Tombari, Fabio Galasso

Abstract:

Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated
them separately. But a closer look unveils important similarities: both tasks target categories that can
only be discriminated by specific object details; and the relevant models should generalize to new categories, not seen during training. We propose a novel unified Query-Guided Network (QGN) applicable
to both tasks. QGN consists of a Query-guided Siamese-Squeeze-and-Excitation subnetwork which reweights both the query and gallery features across all network layers, a Query-guided Region Proposal
subnetwork for query-specific localisation, and a Query-guided Similarity subnetwork for metric learning.
QGN improves on a few recent few-shot fine-grained datasets, outperforming other techniques on CUB
by a large margin. QGN also performs competitively on the person search CUHK-SYSU and PRW datasets,
where we perform in-depth analysis.