research-article

Data-driven Meta-set Based Fine-Grained Visual Recognition

Authors:

Qi WuAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 2372 - 2381

https://doi.org/10.1145/3394171.3414044

Published: 12 October 2020 Publication History

Abstract

Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, learning directly from web images becomes an alternative method for fine-grained visual recognition. However, label noise in the web training set can severely degrade the model performance. To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition. Specifically, guided by a small amount of clean meta-set, we train a selection net in a meta-learning manner to distinguish in- and out-of-distribution noisy images. To further boost the robustness of the model, we also learn a labeling net to correct the labels of in-distribution noisy data. In this way, our proposed method can alleviate the harmful effects caused by out-of-distribution noise and properly exploit the in-distribution noisy samples for training. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to state-of-the-art noise-robust methods.

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Baili NFrigui H(2024)Improving Automatic Target Recognition With Infrared Imagery Using Vision Transformers and Focused Data Augmentation2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647915(381-387)Online publication date: 27-Oct-2024
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Index Terms

Data-driven Meta-set Based Fine-Grained Visual Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2020 ACM.

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Published: 12 October 2020

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Fundamental Research Funds for the Central Universities of China
National Key R&D Program of China
Natural Science Foundation of Jiangsu Province
National Natural Science Foundation of China

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MM '20

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SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Cai ZZhang CHuang DChen YGuan XYao Y(2024)Group benefits instance for data purificationComputers and Electrical Engineering10.1016/j.compeleceng.2024.109156116(109156)Online publication date: May-2024
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Zhang CLin GWang QShen FYao YTang Z(2023)Guided by Meta-Set: A Data-Driven Method for Fine-Grained Visual RecognitionIEEE Transactions on Multimedia10.1109/TMM.2022.318143925(4691-4703)Online publication date: 2023
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Lv XLi MZhang RGao L(2023)CTLC: A Co-Training-Based Label Correction Method for Webly-Supervised LearningNeural Processing Letters10.1007/s11063-023-11425-855:9(12401-12419)Online publication date: 1-Dec-2023
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Shen JYao YHuang SWang ZZhang JWang RYu JLiu T(2023)ProtoSimi: label correction for fine-grained visual categorizationMachine Learning10.1007/s10994-023-06313-0113:4(1903-1920)Online publication date: 28-Mar-2023
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Zhang CWang QXie GWu QShen FTang Z(2022)Robust Learning From Noisy Web Images Via Data Purification for Fine-Grained RecognitionIEEE Transactions on Multimedia10.1109/TMM.2021.313415624(1198-1209)Online publication date: 2022
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