With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval
methods struggle to meet the needs of users seeking access to data across various …

The similarity calculation is too simple in most cross-modal hash retrieval methods, which do
not consider the impact of the relations between instances. To solve this problem, this paper …

Existing cross-modal hash retrieval methods can simultaneously enhance retrieval speed
and reduce storage space. However, these methods face a major challenge in determining …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Cross-modal image-text retrieval (CMITR) has been a high-value research topic for more
than a decade. In most of the previous studies, the data for all tasks are trained as a single …

Continual learning is a research field of artificial neural networks to simulate human lifelong
learning ability. Although a surge of investigations has achieved considerable performance …

Deep cross-modal hash retrieval (DCMHR) methods can effectively analyze the correlation
of multimodal data while maintaining efficiency. However, to pursue better accuracy, most …

To enable machines to mimic human cognitive abilities and alleviate the catastrophic
forgetting problem in cross-modal image-text retrieval (CMITR), this paper proposes a novel …

The recent large-scale pre-trained models like CLIP have aroused great concern in vision-
language tasks. However, when required to match image-text data collected in a streaming …

Visual Question Answering (VQA) has achieved significant success over the last few years,
while most studies focus on training a VQA model on a stationary domain (eg, a given …