Query-Guided Networks for Few-shot Fine-grained Classification and Person Search

• URL: http://arxiv.org/abs/2209.10250v1
• Date: Wed, 21 Sep 2022 10:25:32 GMT
• Title: Query-Guided Networks for Few-shot Fine-grained Classification and Person Search
• Authors: Bharti Munjal and Alessandro Flaborea and Sikandar Amin and Federico Tombari and Fabio Galasso
• Abstract summary: Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. We propose a novel unified Query-Guided Network (QGN) applicable to both tasks. QGN improves on a few recent few-shot fine-grained datasets, outperforming other techniques on CUB by a large margin.
• Score: 93.80556485668731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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