research-article

Revisiting Local Descriptor for Improved Few-Shot Classification

Authors:

Mingliang Xu, and

Qianru SunAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 2s

Article No.: 127, Pages 1 - 23

https://doi.org/10.1145/3511917

Published: 06 October 2022 Publication History

Abstract

Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts have been aimed at designing more and more complex classifiers that measure similarities between query and support images but left the importance of feature embeddings seldom explored. We show that the reliance on sophisticated classifiers is not necessary, and a simple classifier applied directly to improved feature embeddings can instead outperform most of the leading methods in the literature. To this end, we present a new method, named DCAP, for few-shot classification, in which we investigate how one can improve the quality of embeddings by leveraging Dense Classification and Attentive Pooling (DCAP). Specifically, we propose to train a learner on base classes with abundant samples to solve dense classification problem first and then meta-train the learner on plenty of randomly sampled few-shot tasks to adapt it to few-shot scenario or the test time scenario. During meta-training, we suggest to pool feature maps by applying attentive pooling instead of the widely used global average pooling to prepare embeddings for few-shot classification. Attentive pooling learns to reweight local descriptors, explaining what the learner is looking for as evidence for decision making. Experiments on two benchmark datasets show the proposed method to be superior in multiple few-shot settings while being simpler and more explainable. Code is publicly available at https://github.com/Ukeyboard/dcap/.

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Chen HYu YDong YLu ZLi YZhang Z(2024)Multi-Content Interaction Network for Few-Shot SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364385020:6(1-20)Online publication date: 8-Mar-2024
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Wang XJiang BWang XTang JLuo B(2024)Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer Based ApproachIEEE Transactions on Multimedia10.1109/TMM.2023.328313226(1578-1588)Online publication date: 1-Jan-2024
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Huang XChoi S(2024)Bimodal semantic fusion prototypical network for few-shot classificationInformation Fusion10.1016/j.inffus.2024.102421109(102421)Online publication date: Sep-2024
https://doi.org/10.1016/j.inffus.2024.102421
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Index Terms

Revisiting Local Descriptor for Improved Few-Shot Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
        Image representations
  2. Machine learning

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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 2s

June 2022

383 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3561949

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 06 October 2022

Online AM: 18 February 2022

Accepted: 18 January 2022

Revised: 18 December 2021

Received: 20 October 2021

Published in TOMM Volume 18, Issue 2s

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https://dl.acm.org/doi/10.1145/3643850
Wang XJiang BWang XTang JLuo B(2024)Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer Based ApproachIEEE Transactions on Multimedia10.1109/TMM.2023.328313226(1578-1588)Online publication date: 1-Jan-2024
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Huang XChoi S(2024)Bimodal semantic fusion prototypical network for few-shot classificationInformation Fusion10.1016/j.inffus.2024.102421109(102421)Online publication date: Sep-2024
https://doi.org/10.1016/j.inffus.2024.102421
Huang JZhao LYang H(2024)Local descriptor-based spatial cross attention network for few-shot learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02189-1Online publication date: 11-May-2024
https://doi.org/10.1007/s13042-024-02189-1
Song QZhou SChen D(2024)Learning more discriminative local descriptors with parameter-free weighted attention for few-shot learningMachine Vision and Applications10.1007/s00138-024-01551-135:4Online publication date: 28-May-2024
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Jia YWang CZhu WLu KWang TYu K(2023)Cross-channel spatial attention network for few-shot classificationJournal of Electronic Imaging10.1117/1.JEI.32.5.05303632:05Online publication date: 1-Sep-2023
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