Dual Self-Paced Cross-Modal Hashing

Yuan Sun, Jian Dai, Zhenwen Ren, Yingke Chen, Dezhong Peng, Peng Hu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Downloads (Pure)

Abstract

Cross-modal hashing (CMH) is an efficient technique to retrieve relevant data across different modalities, such as images, texts, and videos, which has attracted more and more attention due to its low storage cost and fast query speed. Although existing CMH methods achieve remarkable processes, almost all of them treat all samples of varying difficulty levels without discrimination, thus leaving them vulnerable to noise or outliers. Based on this observation, we reveal and study dual difficulty levels implied in cross-modal hashing learning, i.e., instance-level and feature-level difficulty. To address this problem, we propose a novel Dual Self-Paced Cross-Modal Hashing (DSCMH) that mimics human cognitive learning to learn hashing from 'easy' to 'hard' in both instance and feature levels, thereby embracing robustness against noise/outliers. Specifically, our DSCMH assigns weights to each instance and feature to measure their difficulty or reliability, and then uses these weights to automatically filter out the noisy and irrelevant data points in the original space. By gradually increasing the weights during training, our method can focus on more instances and features from 'easy' to 'hard' in training, thus mitigating the adverse effects of noise or outliers. Extensive experiments are conducted on three widely-used benchmark datasets to demonstrate the effectiveness and robustness of the proposed DSCMH over 12 state-of-the-art CMH methods.

Original languageEnglish
Title of host publicationProceedings of the 38th AAAI Conference on Artificial Intelligence
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
Place of PublicationWashington, DC
PublisherAAAI Press/International Joint Conferences on Artificial Intelligence
Pages15184-15192
Number of pages9
Volume38
Edition14
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

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
ISSN (Print)2159-5399

Conference

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

Cite this