Selective Multi-scale Learning for Object Detection

Junliang Chen, Weizeng Lu, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030863395
Publication statusPublished - 2021
Externally publishedYes
Event30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online
Duration: 14 Sept 202117 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12892 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Conference on Artificial Neural Networks, ICANN 2021
CityVirtual, Online


  • Multi-scale
  • Object detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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