Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval

TL;DR

This paper introduces a lightweight contrastive distilled hashing method for online cross-modal retrieval, effectively bridging offline and online hashing through similarity matrix approximation and knowledge distillation, achieving superior performance.

Contribution

The proposed LCDH method innovatively combines contrastive learning, attention-enhanced feature fusion, and lightweight models to improve online cross-modal hashing performance.

Findings

Outperforms state-of-the-art methods on three datasets

Enhances online hashing accuracy via similarity matrix approximation

Efficiently transfers knowledge from offline to online models

Abstract

Deep online cross-modal hashing has gained much attention from researchers recently, as its promising applications with low storage requirement, fast retrieval efficiency and cross modality adaptive, etc. However, there still exists some technical hurdles that hinder its applications, e.g., 1) how to extract the coexistent semantic relevance of cross-modal data, 2) how to achieve competitive performance when handling the real time data streams, 3) how to transfer the knowledge learned from offline to online training in a lightweight manner. To address these problems, this paper proposes a lightweight contrastive distilled hashing (LCDH) for cross-modal retrieval, by innovatively bridging the offline and online cross-modal hashing by similarity matrix approximation in a knowledge distillation framework. Specifically, in the teacher network, LCDH first extracts the cross-modal features by the contrastive language-image pre-training (CLIP), which are further fed into an attention module for representation enhancement after feature fusion. Then, the output of the attention module is fed into a FC layer to obtain hash codes for aligning the sizes of similarity matrices for online and offline training. In the student network, LCDH extracts the visual and textual features by lightweight models, and then the features are fed into a FC layer to generate binary codes. Finally, by approximating the similarity matrices, the performance of online hashing in the lightweight student network can be enhanced by the supervision of coexistent semantic relevance that is distilled from the teacher network. Experimental results on three widely used datasets demonstrate that LCDH outperforms some state-of-the-art methods.