A comprehensive survey on multi-modal conversational emotion recognition with deep learning
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 …
speaker's emotional state using text, speech, and visual information in the conversation …
Deep imbalanced learning for multimodal emotion recognition in conversations
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 …
emotions in modalities, eg, text, audio, image, and video, which is a significant development …
Sa-gda: Spectral augmentation for graph domain adaptation
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 …
tasks. However, most GNNs are primarily studied under the cases of signal domain with …
Der-gcn: Dialogue and event relation-aware graph convolutional neural network for multimodal dialogue emotion recognition
With the continuous development of deep learning (DL), the task of multimodal dialogue
emotion recognition (MDER) has recently received extensive research attention, which is …
emotion recognition (MDER) has recently received extensive research attention, which is …
Graph information bottleneck for remote sensing segmentation
Y Shou, W Ai, T Meng - arXiv preprint arXiv:2312.02545, 2023 - arxiv.org
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 …
and urban change detection, etc. Despite the success of deep learning-based remote …
CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation
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 …
make inferences about a given person's image or video frame without specific sample data …
Contrastive learning of graphs under label noise
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 …
as a critical task, relying mainly on the information from unlabeled nodes and a minor …
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval
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 …
labeled and unlabeled 2D and 3D data into the same embedding space. The problem is …
Lhact: Rectifying extremely low and high activations for out-of-distribution detection
In recent years, out-of-distribution (OOD) detection has emerged as a crucial research area,
especially when deploying AI products in real-world scenarios. OOD detection researchers …
especially when deploying AI products in real-world scenarios. OOD detection researchers …
Safety in Graph Machine Learning: Threats and Safeguards
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …
years. With their remarkable ability to process graph-structured data, Graph ML techniques …