Multimodal Intent Recognition
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Benchmarks
Most implemented
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Papers
Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition
Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant. Existing methods typically infe…
Multimodal Intent RecognitionTri-Subspaces Disentanglement for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) integrates language, visual, and acoustic modalities to infer human sentiment. Most existing methods either focus on globally shared representations or modality-specific features, whil…
Multimodal Intent RecognitionMultimodal Sentiment AnalysisMVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion
Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We propose MVCL-DAF++, which extends MVCL-DAF with two key modules: (1) Prototype-aware …
Multimodal Intent RecognitionDyKen-Hyena: Dynamic Kernel Generation via Cross-Modal Attention for Multimodal Intent Recognition
Though Multimodal Intent Recognition (MIR) proves effective by utilizing rich information from multiple sources (e.g., language, video, and audio), the potential for intent-irrelevant and conflicting information across m…
Multimodal Intent RecognitionLLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing methods exhibit limitations in their modal…
Multimodal Intent RecognitionRelational ReasoningDeep Learning Approaches for Multimodal Intent Recognition: A Survey
Intent recognition aims to identify users' underlying intentions, traditionally focusing on text in natural language processing. With growing demands for natural human-computer interaction, the field has evolved through …
Multimodal Intent Recognition