This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

Human beings experience life through a spectrum of modes such as vision, taste, hearing,
smell, and touch. These multiple modes are integrated for information processing in our …

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from
contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the …

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although
much progress has been made in bridging vision and language, it remains challenging …

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent
research hotspot due to its ability of enabling fast and flexible information extraction on …

Cross-modal hashing has sparked much attention in large-scale information retrieval for its
storage and query efficiency. Despite the great success achieved by supervised …

Recently, cross-modal retrieval is emerging with the help of deep multimodal learning.
However, even for unimodal data, collecting large-scale well-annotated data is expensive …

Deep multi-view clustering leverages deep neural networks to achieve promising
performance, but almost all existing methods implicitly assume that all views are aligned …

Cross-modal retrieval (CMR) enables flexible retrieval experience across different
modalities (eg, texts versus images), which maximally benefits us from the abundance of …

In this paper, we introduce a novel audio-visual multi-modal bridging framework that can
utilize both audio and visual information, even with uni-modal inputs. We exploit a memory …