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Found 8 papers, 2 papers with code
A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis
Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis but often struggles with irrelevant or misleading visual information.
TECO: Improving Multimodal Intent Recognition with Text Enhancement through Commonsense Knowledge Extraction
The objective of multimodal intent recognition (MIR) is to leverage various modalities-such as text, video, and audio-to detect user intentions, which is crucial for understanding human language and context in dialogue systems.
Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning
Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions.
Bundle Recommendation with Item-level Causation-enhanced Multi-view Learning
To address this, we present BunCa, a novel bundle recommendation approach employing item-level causation-enhanced multi-view learning.
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition
Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing.
Emotion Recognition in Conversation
Multimodal Emotion Recognition
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction
Emotion recognition is a crucial task for human conversation understanding.
Ranked #1 on
Multimodal Emotion Recognition
on IEMOCAP
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization
Multimodal representation learning poses significant challenges in capturing informative and distinct features from multiple modalities.
Vietnamese multi-document summary using subgraph selection approach -- VLSP 2022 AbMuSu Shared Task
Document summarization is a task to generate afluent, condensed summary for a document, andkeep important information.
