Multi-modal conversation emotion recognition (MCER) aims to recognize and track the
speaker's emotional state using text, speech, and visual information in the conversation …

The main task of multimodal emotion recognition in conversations (MERC) is to identify the
emotions in modalities, eg, text, audio, image, and video, which is a significant development …

Remote sensing segmentation has a wide range of applications in environmental protection,
and urban change detection, etc. Despite the success of deep learning-based remote …

Graph neural networks (GNNs) have achieved impressive impressions for graph-related
tasks. However, most GNNs are primarily studied under the cases of signal domain with …

With the continuous development of deep learning (DL), the task of multimodal dialogue
emotion recognition (MDER) has recently received extensive research attention, which is …

In recent years, graph representation learning has achieved remarkable success while
suffering from low-quality data problems. As a mature technology to improve data quality in …

Graph neural networks (GNNs) have achieved great success recently on graph classification
tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels …

Zero-shot age estimation aims to learn feature information about age from input images and
make inferences about a given person's image or video frame without specific sample data …

In the domain of graph-structured data learning, semi-supervised node classification serves
as a critical task, relying mainly on the information from unlabeled nodes and a minor …

This paper studies the problem of semi-supervised 2D-3D retrieval which aims to align both
labeled and unlabeled 2D and 3D data into the same embedding space. The problem is …