Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection
Multimodal fake news detection has become one of the most crucial issues on social media platforms. Although existing methods have achieved advanced performance, two main challenges persist: (1) Under-performed multimodal news information fusion due to model architecture solidification, and (2) weak generalization ability on partial-modality contained fake news. To meet these challenges, we propose a novel and flexible triple path enhanced neural architecture search model MUSE. MUSE includes two dynamic paths for detecting partial-modality contained fake news and a static path for exploiting potential multimodal correlations. Experimental results show that MUSE achieves stable performance improvement over the baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
Fake News DetectionNeural Architecture SearchSimilar Papers 제목 키워드 기반
Can a Robot Walk the Robotic Dog: Triple-Zero Collaborative Navigation for Heterogeneous Multi-Agent Systems
We present Triple Zero Path Planning (TZPP), a collaborative framework for heterogeneous multi-robot systems that requires zero training, zero prior knowledge, and zero simulation. TZPP employs a coordinator--explorer ar…
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between entities and relations of the triplets. However, long-term information amo…
Graph EmbeddingKnowledge Graph EmbeddingNeural Architecture SearchKGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
Entity linking (EL) aligns textual mentions with their corresponding entities in a knowledge base, facilitating various applications such as semantic search and question answering. Recent advances in multimodal entity li…
Entity LinkingKnowledge GraphsRerankingPath-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer. Recent approaches attempt to introduce the knowledge graph embedding …
Graph EmbeddingKnowledge Base Question AnsweringKnowledge Graph EmbeddingKnowledge Graph Embeddings+1MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets
The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and item properties. While incorporating mul…
Model OptimizationMultimodal RecommendationRecommendation Systems