25 citations · 27 across the 8 of their papers we have counts for
8 papers
MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
Shuai Wang, Wangyuan Ding, Yixian Shen +5
Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surface content but struggle with for…
A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding
Shuai Wang, Hongyi Zhu, Jia-Hong Huang +6
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise…
VL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings
Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic +2
Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KG…
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding
Shuai Wang, Ivona Najdenkoska, Hongyi Zhu +4
Understanding visual art requires reasoning across multiple perspectives -- cultural, historical, and stylistic -- beyond mere object recognition. While recent multimodal large lan…
Set2Seq Transformer: Temporal and Position-Aware Set Representations for Sequential Multiple-Instance Learning
Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic +2
In many real-world applications, modeling both the internal structure of sets and their temporal relationships is essential for capturing complex underlying patterns. Sequential mu…
Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Shuai Wang, David W. Zhang, Jia-Hong Huang +4
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural…
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
Shuai Wang, Jiayi Shen, Athanasios Efthymiou +4
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs). Capturing the semantics from such networks requires approaches capable…
Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings
Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic +2
We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-b…