论文标题
FMT：融合人搜索的多任务卷积神经网络
FMT:Fusing Multi-task Convolutional Neural Network for Person Search
论文作者
论文摘要
人搜索是在没有建议和边界框的情况下从图像中检测到的所有人，并从图像中的被检测人员中识别出查询人员，这与人重新识别不同。在本文中，我们提出了一个融合的多任务卷积神经网络（FMT-CNN），以解决检测和重新识别与单个卷积神经网络的相关性和异质性。我们专注于人发现和人重新识别的相互作用如何影响整体绩效。我们在区域提案网络中使用人员标签来生成人员重新识别和人检测网络的功能，这可以同时提高检测和重新识别的准确性。我们还使用多重损失来训练我们的重新识别网络。 Cuhk-Sysu人搜索数据集的实验结果表明，我们所提出的方法的性能优于地图和TOP-1中的最新方法。
Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1.
