research-article

Fine-Grained Adversarial Semi-Supervised Learning

Authors:

Daniele Mugnai,

Federico Pernici,

Francesco Turchini,

Alberto Del BimboAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 1s

Article No.: 34, Pages 1 - 19

https://doi.org/10.1145/3485473

Published: 25 January 2022 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected Version of Record was published on March 18, 2022. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this citation page.

Abstract

In this article, we exploit Semi-Supervised Learning (SSL) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization (FGVC). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows one to back-propagate the information of the parts, represented by second-order pooling, onto unlabeled data in an adversarial training setting. We demonstrate the effectiveness of the combined use by conducting experiments on six state-of-the-art fine-grained datasets, which include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs, and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly show that our proposed method has better performance than the only previous approach that examined this problem; it also obtained higher classification accuracy with respect to the supervised learning methods with which we compared.

Supplementary Material

3485473-vor (3485473-vor.pdf)

Version of Record for "Fine-Grained Adversarial Semi-Supervised Learning" by Mugnai et al., ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 18, No. 1s (TOMM 18:1s).

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https://dl.acm.org/doi/10.1145/3651307
Taha K(2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
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Golmaryami MTaheri RPooranian ZShojafar MXiao P(2022)SETTI: A Self-supervised AdvErsarial Malware DeTection ArchiTecture in an IoT EnvironmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/353642518:2s(1-21)Online publication date: 6-Oct-2022
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Index Terms

Fine-Grained Adversarial Semi-Supervised Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 1s

February 2022

352 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3505206

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Alberto Del Bimbo
University of Firenze, Italy

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Publication History

Published: 25 January 2022

Accepted: 01 September 2021

Revised: 01 July 2021

Received: 01 March 2021

Published in TOMM Volume 18, Issue 1s

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Cited By

Zhao JYang HHe HPeng JZhang WNi JSangaiah ACastiglione A(2024)Backdoor Two-Stream Video Models on Federated LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3651307Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3651307
Taha K(2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102178
Golmaryami MTaheri RPooranian ZShojafar MXiao P(2022)SETTI: A Self-supervised AdvErsarial Malware DeTection ArchiTecture in an IoT EnvironmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/353642518:2s(1-21)Online publication date: 6-Oct-2022
https://dl.acm.org/doi/10.1145/3536425
Tu ZShu XHuang PYan RLiu ZZhang J(undefined)Leveraging Frame- and Feature-Level Progressive Augmentation for Semi-supervised Action RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3655025
https://dl.acm.org/doi/10.1145/3655025

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