The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …

Programming robot behavior in a complex world faces challenges on multiple levels, from
dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large …

The problem of missing modalities is both critical and non-trivial to be handled in multi-
modal models. It is common for multi-modal tasks that certain modalities contribute more …

Automatically recognising apparent emotions from face and voice is hard, in part because of
various sources of uncertainty, including in the input data and the labels used in a machine …

Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as
it integrates information from different sources to complement their respective performances …

Objective. Endometriosis, affecting about 10% of individuals assigned female at birth, is
challenging to diagnose and manage. Diagnosis typically involves the identification of …

Recently, multi-modal entity alignment has emerged as a pivotal endeavor for the integration
of Multi-Modal Knowledge Graphs (MMKGs) originating from diverse data sources. Existing …

While multi-modal deep learning approaches trained using magnetic resonance imaging
(MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown …

Systems for multimodal emotion recognition (ER) are commonly trained to extract features
from different modalities (eg, visual, audio, and textual) that are combined to predict …

In applications involving analysis of the wearable sensor data, machine learning techniques
that use features from the topological data analysis (TDA) have demonstrated remarkable …