Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.

Multimodal Deep Learning is a machine learning subfield that aims to train AI models to process and find relationships between different types of data (modalities)—typically, images, video, …

Sep 9, 2024 · In this study, we present a dual-extraction modeling (DEM) architecture that incorporates a multi-head self-attention mechanism and a fully connected feedforward neural …

In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep …

Jan 12, 2023 · This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art …

Nov 28, 2024 · Multi-modal clustering represents a formidable challenge in the domain of unsupervised learning. The objective of multi-modal clustering is to categorize data collected …

6 days ago · However, most existing CSS algorithms primarily focus on increasing the cooperative detection accuracy, while neglecting the computational complexity or sensing latency. …

Feb 11, 2025 · We have theoretically and experimentally proved the effectiveness of the proposed paradigm in overcoming the multi-model forgetting. Besides, we apply the proposed paradigm …

In this second phase, Multi-Shield uses the CLIP model as a zero-shot vision-language classifier [17]. It compares the visual representation of the input image with class description prompts to …

May 2, 2025 · To optimize the model performance in terms of accuracy and model complexity, the hyperparameters of each algorithm are selected using the Bayesian optimization algorithm. …