Enhancing Robustness of Prototype with Attentive Information Guided Alignment in Few-Shot Classification

Tae Hyung Kim, Woo Jeoung Nam, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we carefully revisit the issues of conventional few-shot learning: i) gaps in highlighted features between objects in support and query samples, and ii) losing the explicit local properties due to global pooled features. Motivated by them, we propose a novel method to enhance robustness in few-shot learning by aligning prototypes with abundantly informed ones. As a way of providing more information, we smoothly augment the support image by carefully manipulating the discriminative part corresponding to the highest attention score to consistently represent the object without distorting the original information. In addition, we leverage word embeddings of each class label to provide abundant feature information, serving as the basis for closing gaps between prototypes of different branches. The two parallel branches of explicit attention modules independently refine support prototypes and information-rich prototypes. Then, the support prototypes are aligned with superior prototypes to mimic rich knowledge of attention-based smooth augmentation and word embeddings. We transfer the imitated knowledge to queries in a task-adaptive manner and cross-adapt the queries and prototypes to generate crucial features for metric-based few-shot learning. Extensive experiments demonstrate that our method consistently outperforms existing methods on four benchmark datasets.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings
EditorsHisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages183-194
Number of pages12
ISBN (Print)9783031333736
DOIs
Publication statusPublished - 2023
Event27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 - Osaka, Japan
Duration: 2023 May 252023 May 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13935 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
Country/TerritoryJapan
CityOsaka
Period23/5/2523/5/28

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Attention mechanism
  • Data augmentation
  • Few-shot classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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