# Deep Cross-Modal Hashing Source: [Deep Cross-Modal Hashing]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:17:31.370Z Authors: Qing-Yuan Jiang, Wu-Jun Li Published: 2016-02-06T13:43:24.000Z ## Abstract Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, almost all existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with handcrafted features may not achieve satisfactory performance. In this paper, we propose a novel cross-modal hashing method, called deep crossmodal hashing (DCMH), by integrating feature learning and hash-code learning into the same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on two real datasets with text-image modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Text-to-Image Retrieval on MIRFlickr-25K (test)](): Text-to-Image Retrieval; MIRFlickr-25K (test); mAP@50: 73.5. - [Image-to-Text Retrieval on MIRFlickr-25K (test)](): Image-to-Text Retrieval; MIRFlickr-25K (test); mAP@50: 75.1. ## Original sources and implementation [Original paper]() [PDF]()