POLISH: Adaptive Online Cross-Modal Hashing for Class Incremental Data

Yu-Wei Zhan, Xin Luo, Zhen-Duo Chen, Yongxin Wang, Yinwei Wei, Xin-Shun Xu

Keywords: Cross-modal Retrieval, Learning to Hash, Online Hashing, Efficient Discrete Optimization

Abstract:

In recent years, hashing-based online cross-modal retrieval has garnered growing attention. This trend is motivated by the fact that web data is increasingly delivered in a streaming manner as opposed to batch processing. Simultaneously, the sheer scale of web data sometimes makes it impractical to fully load for the training of hashing models. Despite the evolution of online cross-modal hashing techniques, several challenges remain: 1) Most existing methods learn hash codes by considering the relevance among newly arriving data or between new data and the existing data, often disregarding valuable global semantic information. 2) A common but limiting assumption in many methods is that the label space remains constant, implying that all class labels should be provided within the first data chunk. This assumption does not hold in real-world scenarios, and the presence of new labels in incoming data chunks can severely degrade or even break these methods.

To tackle these issues, we introduce a novel supervised online cross-modal hashing method named adaPtive Online cLass-Incremental haSHing (POLISH). Leveraging insights from language models, POLISH generates representations for new class label from multiple angles. Meanwhile, POLISH treats label embeddings, which remain unchanged once learned, as stable global information to produce high-quality hash codes. POLISH also puts forward an efficient optimization algorithm for hash code learning. Extensive experiments on two real-world benchmark datasets show the effectiveness of the proposed POLISH for class incremental data in the cross-modal hashing domain.

Track: Web Mining and Content Analysis

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Submission Number: 2504