论文标题
画廊过滤网络搜索
Gallery Filter Network for Person Search
论文作者
论文摘要
在亲自搜索的情况下，我们的目标是在其他画廊场景中从一个场景中定位一个查询人员。此搜索操作的成本取决于画廊场景的数量，从而减少可能场景的池有益。我们描述并演示画廊过滤网络（GFN），这是一个新型模块，可以从搜索过程中有效丢弃画廊场景，并为在其余场景中检测到的人的好处评分。我们表明，通过在不同的检索集（包括跨相机，遮挡和低分辨率方案）上测试不同的检索集，GFN在一系列不同的条件下是可靠的。此外，我们开发了基本Seqnext人搜索模型，该模型改进并简化了原始的Seqnet模型。我们表明，SEQNEXT+GFN组合在标准PRW和CUHK-SYSU搜索数据集上的其他最先进的方法上产生了显着的性能提高。为了帮助实验和其他模型，我们为通常用于人搜索研究的数据处理和评估管道提供标准化工具。
In person search, we aim to localize a query person from one scene in other gallery scenes. The cost of this search operation is dependent on the number of gallery scenes, making it beneficial to reduce the pool of likely scenes. We describe and demonstrate the Gallery Filter Network (GFN), a novel module which can efficiently discard gallery scenes from the search process, and benefit scoring for persons detected in remaining scenes. We show that the GFN is robust under a range of different conditions by testing on different retrieval sets, including cross-camera, occluded, and low-resolution scenarios. In addition, we develop the base SeqNeXt person search model, which improves and simplifies the original SeqNet model. We show that the SeqNeXt+GFN combination yields significant performance gains over other state-of-the-art methods on the standard PRW and CUHK-SYSU person search datasets. To aid experimentation for this and other models, we provide standardized tooling for the data processing and evaluation pipeline typically used for person search research.
