Query-guided networks for few-shot fine-grained classification and person search. (January 2023) - Munjal, Bharti; Flaborea, Alessandro; Amin, Sikandar; Tombari, Federico; Galasso, Fabio
Highlights: Prior works treat few-shot fine-grained classification and person search separately. The proposed query-guided networks (QGN) address both tasks in a unified framework. QGN introduces query-guidance for the task of few-shot fine-grained recognition. State-of-the-art performance on CUB, FGVC-Aircraft, Stanford Dogs datasets. The paper performs an in-depth analysis of each of the query-guided components of QGN. Graphical abstract: 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 re-weights 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.