Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Multi-modal clustering (MMC) aims to explore complementary information from diverse
modalities for clustering performance facilitating. This article studies challenging problems in …

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

Multi-modal fusion approaches aim to integrate information from different data sources.
Unlike natural datasets, such as in audio-visual applications, where samples consist of …

Abstract We introduce Mind Artist (MindArt) a novel and efficient neural decoding
architecture to snap artistic photographs from our mind in a controllable manner. Recently …

Abstract In Re-identification (ReID) recent advancements yield noteworthy progress in both
unimodal and cross-modal retrieval tasks. However the challenge persists in developing a …

Designing more efficient, reliable, and explainable neural network architectures is critical to
studies that are based on artificial intelligence (AI) techniques. Previous studies, by post-hoc …

Text-to-audio grounding (TAG) task aims to predict the onsets and offsets of sound events
described by natural language. This task can facilitate applications such as multimodal …

Regression plays an essential role in many medical imaging applications for estimating
various clinical risk or measurement scores. While training strategies and loss functions …

Cross-domain data analysis is playing an increasingly important role in media convergence
and can be adopted for many applications. Most existing methods consider the domain …