research-article

Target-guided Adaptive Base Class Reweighting for Few-Shot Learning

Authors:

Xianming LiuAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 5335 - 5343

https://doi.org/10.1145/3474085.3475656

Published: 17 October 2021 Publication History

Abstract

For few-shot learning, minimizing the empirical risk cannot reach the optimal hypothesis from image to its label due to the effect of overfitting. Therefore, most of the existing work leverages a set of base classes with sufficient labeled samples to pre-train a general encoder for feature representation, which is then applied for all few-shot classification tasks without considering the uniqueness of the target task. We suppose that different base classes help solve a target task in varying degrees, and some classes even introduce a negative effect. To this end, we propose a Target-guided Base Class Reweighting (TBR) approach, which uses a reweighting-in-the-loop optimization algorithm to assign a set of weights for base classes adaptively given a target task. Specifically, TBR learns the parameter of the encoder via minimizing weighted empirical risk on base class data, then optimizes the weights according to the the encoder's performance on support set of the target task. Such an alternating optimization procedure brings reweighting into the loop which makes the encoder more sensitive to the novel classes of the target task. Extensive experiments demonstrate that the proposed method can improve the performance of model-based approaches on two few-shot classification benchmarks.

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Cited By

Huang JCao JLin LZhang D(2023)IRA-FSOD: Instant-Response and Accurate Few-Shot Object DetectorIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327261233:11(6912-6923)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3272612

Index Terms

Target-guided Adaptive Base Class Reweighting for Few-Shot Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
        Image representations
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning

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Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

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Published: 17 October 2021

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Natural Science Foundation of China

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MM '21

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MM '21: ACM Multimedia Conference

October 20 - 24, 2021

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Cited By

Huang JCao JLin LZhang D(2023)IRA-FSOD: Instant-Response and Accurate Few-Shot Object DetectorIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327261233:11(6912-6923)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3272612

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