Feature Fusion from Head to Tail for Long-Tailed Visual Recognition

Mengke Li, Zhikai Hu, Yang Lu, Weichao Lan, Yiu Ming Cheung, Hui Huang*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head classes but largely disregard tail classes. The biased decision boundary caused by inadequate semantic information in tail classes is one of the key factors contributing to their low recognition accuracy. To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T). We replace a portion of feature maps from tail classes with those belonging to head classes. These fused features substantially enhance the diversity of tail classes. Both theoretical analysis and practical experimentation demonstrate that H2T can contribute to a more optimized solution for the decision boundary. We seamlessly integrate H2T in the classifier adjustment stage, making it a plug-and-play module. Its simplicity and ease of implementation allow for smooth integration with existing long-tailed recognition methods, facilitating a further performance boost. Extensive experiments on various long-tailed benchmarks demonstrate the effectiveness of the proposed H2T. The source code is available at https://github.com/Keke921/H2T.

Original languageEnglish
Title of host publicationProceedings of the 38th AAAI Conference on Artificial Intelligence
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
PublisherAAAI press
Pages13581-13589
Number of pages9
ISBN (Print)1577358872, 9781577358879
DOIs
Publication statusPublished - 25 Mar 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024
https://ojs.aaai.org/index.php/AAAI/issue/archive

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number12
Volume38
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference38th AAAI Conference on Artificial Intelligence, AAAI 2024
Country/TerritoryCanada
CityVancouver
Period20/02/2427/02/24
Internet address

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • ML: Classification and Regression
  • CV: Object Detection & Categorization
  • ML: Multi-class/Multi-label Learning & Extreme Classification

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