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Glocal Alignment for Unsupervised Domain Adaptation

Authors:

Sachin Chhabra,

Prabal Bijoy Dutta,

Hemanth VenkateswaraAuthors Info & Claims

MULL'21: Multimedia Understanding with Less Labeling on Multimedia Understanding with Less Labeling

Pages 45 - 51

https://doi.org/10.1145/3476098.3485051

Published: 20 October 2021 Publication History

Abstract

Traditional unsupervised domain adaptation methods attempt to align source and target domains globally and are agnostic to the categories of the data points. This results in an inaccurate categorical alignment and diminishes the classification performance on the target domain. In this paper, we alter existing adversarial domain alignment methods to adhere to category alignment by imputing category information. We partition the samples based on category using source labels and target pseudo labels and then apply domain alignment for every category. Our proposed modification provides a boost in performance even with a modest pseudo label estimator. We evaluate our approach on 4 popular domain alignment loss functions using object recognition and digit datasets.

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Cited By

Pei JMen ALiu YZhuang XChen Q(2024)Evidential Multi-Source-Free Unsupervised Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.336197846:8(5288-5305)Online publication date: Aug-2024
https://doi.org/10.1109/TPAMI.2024.3361978
Chhabra SVenkateswara HLi B(2023)Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00411(4114-4123)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00411
Pei JJiang ZMen AChen LLiu YChen Q(2023)Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain AdaptationIEEE Transactions on Image Processing10.1109/TIP.2023.325875332(2033-2048)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3258753

Index Terms

Glocal Alignment for Unsupervised Domain Adaptation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
        Image representations

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Information

Published In

cover image ACM Conferences

MULL'21: Multimedia Understanding with Less Labeling on Multimedia Understanding with Less Labeling

October 2021

64 pages

ISBN:9781450386814

DOI:10.1145/3476098

Program Chairs:
Xiu-Shen Wei
Nanjing University of Science and Technology, China
,
Han-Jia Ye
Nanjing University, China
,
Jufeng Yang
Nankai University, China
,
Jian Yang
Nanjing University of Science and Technology, China

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2021

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Office of Naval Research

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MM '21

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SIGMM

MM '21: ACM Multimedia Conference

October 24, 2021

Virtual Event, China

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Cited By

Pei JMen ALiu YZhuang XChen Q(2024)Evidential Multi-Source-Free Unsupervised Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.336197846:8(5288-5305)Online publication date: Aug-2024
https://doi.org/10.1109/TPAMI.2024.3361978
Chhabra SVenkateswara HLi B(2023)Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00411(4114-4123)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00411
Pei JJiang ZMen AChen LLiu YChen Q(2023)Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain AdaptationIEEE Transactions on Image Processing10.1109/TIP.2023.325875332(2033-2048)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3258753

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