Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning
The first Multimodal Emotion Recognition Challenge (MER 2023) 1 was successfully held at
ACM Multimedia. The challenge focuses on system robustness and consists of three distinct …
ACM Multimedia. The challenge focuses on system robustness and consists of three distinct …
GCNet: Graph completion network for incomplete multimodal learning in conversation
Conversations have become a critical data format on social media platforms. Understanding
conversation from emotion, content and other aspects also attracts increasing attention from …
conversation from emotion, content and other aspects also attracts increasing attention from …
Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis
With the proliferation of user-generated online videos, Multimodal Sentiment Analysis (MSA)
has attracted increasing attention recently. Despite significant progress, there are still two …
has attracted increasing attention recently. Despite significant progress, there are still two …
Multimodal information bottleneck: Learning minimal sufficient unimodal and multimodal representations
Learning effective joint embedding for cross-modal data has always been a focus in the field
of multimodal machine learning. We argue that during multimodal fusion, the generated …
of multimodal machine learning. We argue that during multimodal fusion, the generated …
Modality translation-based multimodal sentiment analysis under uncertain missing modalities
Z Liu, B Zhou, D Chu, Y Sun, L Meng - Information Fusion, 2024 - Elsevier
Multimodal sentiment analysis (MSA) with uncertain missing modalities poses a new
challenge in sentiment analysis. To address this problem, efficient MSA models that …
challenge in sentiment analysis. To address this problem, efficient MSA models that …
Multimodal representation learning by alternating unimodal adaptation
Multimodal learning which integrates data from diverse sensory modes plays a pivotal role
in artificial intelligence. However existing multimodal learning methods often struggle with …
in artificial intelligence. However existing multimodal learning methods often struggle with …
Multimodal sentiment analysis using deep learning and fuzzy logic: A comprehensive survey
Multimodal sentiment analysis (MSA) is the process of identifying sentiment polarities that
users may simultaneously display in text, audio, and video data. Sentiment analysis …
users may simultaneously display in text, audio, and video data. Sentiment analysis …
Tag-assisted multimodal sentiment analysis under uncertain missing modalities
Multimodal sentiment analysis has been studied under the assumption that all modalities
are available. However, such a strong assumption does not always hold in practice, and …
are available. However, such a strong assumption does not always hold in practice, and …
Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities
Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …
machine learning supporting integrated modeling of structured and unstructured data is an …
Counterfactual reasoning for out-of-distribution multimodal sentiment analysis
Existing studies on multimodal sentiment analysis heavily rely on textual modality and
unavoidably induce the spurious correlations between textual words and sentiment labels …
unavoidably induce the spurious correlations between textual words and sentiment labels …