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Multimodal Intent Recognition
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Intent recognition on multimodal content.
Image source: MIntRec: A New Dataset for Multimodal Intent Recognition
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition
TECO: Improving Multimodal Intent Recognition with Text Enhancement through Commonsense Knowledge Extraction
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
MIntRec: A New Dataset for Multimodal Intent Recognition
0/1 Deep Neural Networks via Block Coordinate Descent
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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