In autonomous driving, robotics, and augmented reality, accurate localization remains one of the most challenging problems. Traditional ...
Multimodal large language models have shown powerful abilities to understand and reason across text and images, but their ...
Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...
Abstract: How to effectively interact audio with vision has garnered considerable interest within the multi-modality research field. Recently, a novel audio-visual video segmentation (AVS) task has ...
🤔 Can autoregressive visual generation supervision improve VLMs' understanding capability? 🚀 Reconstructing the visual semantics of images leads to better visual comprehension. Abstract. Typical ...
ABSTRACT: As morphemes are the smallest phonetic and semantic word formation units in Chinese, the study of morphemes has always been an important part of Chinese language acquisition research. Taking ...
This article describes a combined visual and haptic localization experiment that addresses the area of multimodal cueing. The aim of the present investigation was to characterize two-dimensional (2D) ...
Abstract: Although audio-visual speech separation has achieved significant advancements, it is relatively difficult to obtain audio and visual modalities simultaneously in real scenarios, often ...
The congruency sequence effect (CSE) refers to the reduction in the congruency effect in the current trial after an incongruent trial compared with a congruent trial. Although previous studies widely ...
Omni-modal large language models (LLMs) are at the forefront of artificial intelligence research, seeking to unify multiple data modalities such as vision, language, and speech. The primary goal is to ...
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