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Deep Unsupervised Hybrid-similarity Hadamard Hashing

Authors:

Dan MengAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 3274 - 3282

https://doi.org/10.1145/3394171.3414028

Published: 12 October 2020 Publication History

Abstract

Hashing has become increasingly important for large-scale image retrieval. Recently, deep supervised hashing has shown promising performance, yet little work has been done under the more realistic unsupervised setting. The most challenging problem in unsupervised hashing methods is the lack of supervised information. Besides, existing methods fail to distinguish image pairs with different similarity degrees, which leads to a suboptimal construction of similarity matrix. In this paper, we propose a simple yet effective unsupervised hashing method, dubbed Deep Unsupervised Hybrid-similarity Hadamard Hashing (DU3H), which tackles these issues in an end-to-end deep hashing framework. DU3H employs orthogonal Hadamard codes to provide auxiliary supervised information in unsupervised setting, which can maximally satisfy the independence and balance properties of hash codes. Moreover, DU3H utilizes both highly and normally confident image pairs to jointly construct a hybrid-similarity matrix, which can magnify the impacts of different pairs to better preserve the semantic relations between images. Extensive experiments conducted on three widely used benchmarks validate the superiority of DU3H.

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Deep Unsupervised Hybrid-similarity Hadamard Hashing

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Cited By

He LHuang ZLiu JChen EWang FSha JWang SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic HashingProceedings of the ACM Web Conference 202410.1145/3589334.3645440(1395-1406)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645440
Wang HJiang HSun JZhang SChen CHua XLuo X(2024)DIOR: Learning to Hash With Label Noise Via Dual Partition and Contrastive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331210936:4(1502-1517)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3312109
Zhang XLiu XNie XKang XYin Y(2024)Semi-Supervised Semi-Paired Cross-Modal HashingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331238534:7(6517-6529)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2023.3312385
Show More Cited By

Index Terms

Deep Unsupervised Hybrid-similarity Hadamard Hashing
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
    2. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Image search
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2020 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

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Published: 12 October 2020

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The National Key Research and Development Program of China
The Strategic Priority Research Program of the Chinese Academy of Sciences

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MM '20

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SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

He LHuang ZLiu JChen EWang FSha JWang SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic HashingProceedings of the ACM Web Conference 202410.1145/3589334.3645440(1395-1406)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645440
Wang HJiang HSun JZhang SChen CHua XLuo X(2024)DIOR: Learning to Hash With Label Noise Via Dual Partition and Contrastive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331210936:4(1502-1517)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3312109
Zhang XLiu XNie XKang XYin Y(2024)Semi-Supervised Semi-Paired Cross-Modal HashingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331238534:7(6517-6529)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2023.3312385
Cui HLi FZhu LLi JZhang Z(2024)Online Query Expansion Hashing for Efficient Image RetrievalIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329641234:3(1941-1953)Online publication date: Mar-2024
https://doi.org/10.1109/TCSVT.2023.3296412
Zhu FZhang WWu DWang LLi BWang W(2024)Exploring Targeted Universal Adversarial Attack for Deep HashingICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10445806(3335-3339)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10445806
Hu FZhang CGuo JWei XZhao LXu AGao L(2024)An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01671(17648-17657)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01671
Zhu FZhang WWu DWang LLi BWang W(2023)Targeted Transferable Attack against Deep Hashing RetrievalProceedings of the 5th ACM International Conference on Multimedia in Asia10.1145/3595916.3626420(1-7)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3595916.3626420
Song ZSu QChen JEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Unsupervised Hashing with Contrastive Learning by Exploiting Similarity Knowledge and Hidden Structure of DataProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612596(6350-6358)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612596
Zhang CLuo XZhan YZhang PChen ZWang YYang XXu XEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Self-Distillation Dual-Memory Online Hashing with Hash Centers for Streaming Data RetrievalProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612119(6340-6349)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612119
Wang HSun JWei XZhang SChen CHua XLuo X(2023)DANCE: Learning A Domain Adaptive Framework for Deep HashingProceedings of the ACM Web Conference 202310.1145/3543507.3583445(3319-3330)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583445
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