Query-guided networks for few-shot fine-grained classification and person search. (January 2023)

• Record Type: Journal Article
• Authors: Munjal, Bharti; Flaborea, Alessandro; Amin, Sikandar; Tombari, Federico; Galasso, Fabio
• Is Part Of: Pattern recognition. Volume 133(2023)
• Publication Date: 2023-01
• Subjects: Meta-learning -- Few-shot learning -- Fine-grained classification -- Person search -- Person re-identification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109049
• Languages: English
• ISSNs: 0031-3203

Abstract

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.