With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

Neural ranking models (NRMs) have shown remarkable success in recent years, especially
with pre-trained language models. However, deep neural models are notorious for their …

Deep cross-modal hashing has achieved excellent retrieval performance with the powerful
representation capability of deep neural networks. Regrettably, current methods are …

Vision-language pre-trained (VLP) models have been the foundation of numerous vision-
language tasks. Given their prevalence, it becomes imperative to assess their adversarial …

Deep cross-modal hashing has promoted the field of multi-modal retrieval due to its
excellent efficiency and storage, but its vulnerability to backdoor attacks is rarely studied …

Deep Hashing (DH) based image retrieval has been widely applied to face-matching
systems due to its accuracy and efficiency. However, this convenience comes with an …

Unsupervised cross-modal hashing retrieval across image and text modality is a challenging
task because of the suboptimality of similarity guidance, ie, the joint similarity matrix …

With the advancement of deep learning, the task of image-text retrieval has received
widespread attention for addressing the semantic heterogeneity in multimodal data …

Frequent cloud data breaches cause irreparable damage to cloud users and providers.
Cross-media retrieval can better leverage the value of data, but existing cross-media …

Deep cross-modal retrieval, with its effective and efficient search capabilities, has gained
widespread adoption in today's media-sharing practices yet raises concerns regarding …