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Feature pyramid network (FPN) is a typical structure in object detection. It can improve the accuracy of detection results by fusing feature information at different resolutions and enhancing the expression ability of different levels of features. Among them, the mismatch between the resolution of feature information and the receptive field and the limited way of feature fusion hinder the full exchange of feature information. To solve the above problems, this paper designs a new structure called an object detection feature pyramid network based on context information and an efficient bidirectional fusion (CB-FPN): (1) Before feature fusion, this study designs a context enhancement module with cross stage partial network (CSPNet) module (CEM-CSP). By using carefully designed dilated convolutions on high-level features, rich context information and receptive fields are obtained to match appropriate feature information. (2) In feature fusion, this study designed a bidirectional efficient feature pyramid network (BE-FPN) module to fuse features efficiently. After adding these two modified architectures to Faster R-CNN with ResNet-50, the average precision (AP) improves from 37.5 to 39.2 on COCO val-2017 data set. In addition, extensive experiments show the effectiveness of our methods on one-stage, two-stage, and anchor-free models. [ABSTRACT FROM AUTHOR]

Face verification models based on centralized training on large face datasets have achieved excellent performance on various test benchmarks. However, due to the increasingly sophisticated privacy protection law, centrally collecting large amount of face images becomes more difficult. We consider learning a face verification model in the federated setting, where each client has access to the face images of only one class and class embeddings cannot be shared to other clients because of data privacy. In this paper, we propose Federated face verification (FedFV), in which server transfers some equivalent class embeddings to clients so that the clients' class embeddings can be separated far away from each other. We show that our proposed method FedFV outperforms the existing approaches in several face verification benchmarks. [ABSTRACT FROM AUTHOR]