@inproceedings{268d353100a443708d6219bb26ef0278,
title = "Selective Multi-scale Learning for Object Detection",
abstract = "Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8% improvement in AP (from 39.1% to 40.9%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0% improvement in AP.",
keywords = "Multi-scale, Object detection",
author = "Junliang Chen and Weizeng Lu and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 30th International Conference on Artificial Neural Networks, ICANN 2021 ; Conference date: 14-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1007/978-3-030-86340-1_1",
language = "English",
isbn = "9783030863395",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--14",
editor = "Igor Farka{\v s} and Paolo Masulli and Sebastian Otte and Stefan Wermter",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings",
address = "Germany",
}