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Multimodal Intent Recognition

Intent recognition on multimodal content.

Image source: MIntRec: A New Dataset for Multimodal Intent Recognition

Papers

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Showing 1–16 of 16 papers

TitleStatusHype
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain ConversationCode2
ALBERT: A Lite BERT for Self-supervised Learning of Language RepresentationsCode2
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in ConversationsCode2
ViLT: Vision-and-Language Transformer Without Convolution or Region SupervisionCode1
MIntRec: A New Dataset for Multimodal Intent RecognitionCode1
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent RecognitionCode1
A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
WDMIR: Wavelet-Driven Multimodal Intent Recognition—0
EMOE: Modality-Specific Enhanced Dynamic Emotion Experts—0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
TECO: Improving Multimodal Intent Recognition with Text Enhancement through Commonsense Knowledge Extraction—0
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional ExpertsCode0
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal AlignmentCode0
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