Audio-visual emotion recognition is the research of identifying human emotional states by
combining the audio modality and the visual modality simultaneously, which plays an …

We consider the problem of identifying universal low-dimensional features from high-
dimensional data for inference tasks in settings involving learning. For such problems, we …

Audio-visual emotion recognition aims to distinguish human emotional states by integrating
the audio and visual data acquired in the expression of emotions. It is crucial for facilitating …

This paper investigates remote sensing data recognition and classification with multimodal
data fusion. Aiming at the problems of low recognition and classification accuracy and the …

Most of the existing studies regarding cross-modal content-based remote sensing image
retrieval (CM-CBRSIR) focus on reducing/enlarging the Euclidean distances of cross-modal …

It has already been observed that audio-visual embedding is more robust than uni-modality
embedding for person verification. But the relationship of keyframes in time series between …

It has already been observed that audio-visual embedding is more robust than uni-modality
embedding for person verification. Here, we proposed a novel audio-visual strategy that …

A real-world application or setting involves interaction between different modalities (eg,
video, speech, text). In order to process the multimodal information automatically and use it …

The vanilla fusion methods still dominate a large percentage of mainstream audio-visual
tasks. However, the effectiveness of vanilla fusion from a theoretical perspective is still worth …

In this paper, we propose a semi-supervised learning approach for the Visual Question
Answering (VQA) task based on maximal correlation. Instead of training the VQA model with …