@inproceedings{753abaf918ee40c9a5d215370fee395a,
title = "Scale Semantic Flow Preserving Across Image Pyramid",
abstract = "Image pyramid based face detector is powerful yet time consuming when cooperated with convolutional neural network, and thus hard to satisfy the computational requirement in real-world applications. In this paper, we proposed a novel method using Semantic Preserving Feature Pyramid (SPFP) to eliminate the computational gap between image pyramid and feature pyramid based detectors. Since a feature tensor of an image can be upsampled or downsampled by Reversible Scale Semantic Flow Preserving (RS2FP ) network, we do not need to feed images with all scales but a middle scale into the network. Extensive experiments demonstrate that the proposed algorithm can accelerate image pyramid by about 5 × to 7 × on widely used face detection benchmarks while maintaining the comparable performance.",
keywords = "Face detection, Scale attention, Semantic preserving",
author = "Zhili Lin and Guanglu Song and Biao Leng",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1007/978-3-030-92307-5\_54",
language = "英语",
isbn = "9783030923068",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "464--471",
editor = "Teddy Mantoro and Minho Lee and Ayu, \{Media Anugerah\} and Wong, \{Kok Wai\} and Hidayanto, \{Achmad Nizar\}",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
address = "德国",
}