论文标题
查询引导的网络，用于几个射击细粒度分类和人搜索
Query-Guided Networks for Few-shot Fine-grained Classification and Person Search
论文作者
论文摘要
很少有细粒度的分类和人搜索作为独特的任务和文学作品，已经分别对待了它们。但是，仔细观察揭示了重要的相似之处：这两个任务的目标类别只能被特定的对象细节歧视；相关模型应概括为新类别，在培训期间看不到。 我们提出了一个适用于这两个任务的新型统一查询引导网络（QGN）。 QGN由一个查询引导的暹罗语和兴奋子网组成，该子网重新进行了所有网络层的查询和画廊功能，一个查询定位的查询区域建议特定于特定于特定的定位的子网络以及查询指导的相似性相似性子网络用于指标学习。 QGN在最近的一些几个细颗粒数据集上有所改善，在幼崽上的其他技术的表现要大。 QGN还对人搜索Cuhk-Sysu和PRW数据集进行了竞争性执行，我们在其中进行了深入的分析。
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.
