Learning a Weight Map for Weakly-Supervised Localization

Tal Shaharabany*, Lior Wolf

*Corresponding author for this work

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

Abstract

In the weakly supervised localization setting, supervision is given as an image-level label. We propose employing an image classifier f and training a generative network g that outputs, given the input image, a per-pixel weight map that indicates the location of the object within the image. Network g is trained by minimizing the discrepancy between the output of the classifier f on the original image and its output given the same image weighted by the output of g. Our results indicate that the method outperforms existing localization methods on the challenging fine-grained classification datasets.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23

Funding

FundersFunder number
Horizon 2020 Framework ProgrammeERC CoG 725974
European Research Council

    Fingerprint

    Dive into the research topics of 'Learning a Weight Map for Weakly-Supervised Localization'. Together they form a unique fingerprint.

    Cite this