Multimodal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 9; pp. 6289 - 6302
• Main Authors: Hoang, Tuan, Do, Thanh-Toan, Nguyen, Tam V., Cheung, Ngai-Man
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Binary codes; Correlation; Cross-modal retrieval; Intermodal; Lower bounds; Matrix decomposition; Maximization; multi-modal; mutual information (MI); Optimization; Representation learning; Representations; Retrieval; Semantics; Similarity; Task analysis; Training; unsupervised hashing; Unsupervised learning; variational information maximization
• DOI: 10.1109/TNNLS.2021.3135420
• ISSN: 2162-237X

In this article, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed cross-modal info-max hashing (CMIMH). First, to learn informative representations that can preserve both intramodal and intermodal similarities, we leverage the recent advances in estimating variational lower bound of MI to maximizing the MI between the binary representations and input features and between binary representations of different modalities. By jointly maximizing these MIs under the assumption that the binary representations are modeled by multivariate Bernoulli distributions, we can learn binary representations, which can preserve both intramodal and intermodal similarities, effectively in a mini-batch manner with gradient descent. Furthermore, we find out that trying to minimize the modality gap by learning similar binary representations for the same instance from different modalities could result in less informative representations. Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks. Quantitative evaluations on standard benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.