Factorized Transformer Hashing with Adaptive Routing for Large-scale Image Retrieval

摘要

Transformer architecture has driven significant advancements in deep hashing, establishing itself as a dominant framework for large-scale retrieval and storage applications. However, existing Transformer-based deep hashing methods typically employ unvarying feature transformations across all images, limiting their adaptability to diverse visual patterns. This rigidity restricts the model's capacity to learn both highly distinctive and generalizable discrete representations, posing challenges for retrieval in open-world scenarios. To overcome this challenge, we propose a novel Factorized Transformer Hashing (FTH) framework, which introduces a factorized transformer to enhance the generalization and discriminative power of hash codes. Specifically, we decompose the Multi-Head Self-Attention (MHSA) and Multi-Layer Perceptron (MLP) blocks into multiple sub-blocks, forming a transformer factorization scheme that captures diverse feature characteristics through independent sub-blocks. Furthermore, we develop an adaptive selection strategy, leveraging a set of learnable selectors with the Softmax function, to dynamically route each image to the most appropriate sub-block for processing. Extensive experiments on three benchmark datasets demonstrate that the proposed FTH framework significantly outperforms state-of-the-art baselines in both image hashing and zero-shot hashing tasks. Source code is available at https://github.com/QinLab-WFU/FTH.