3 papers
cs.IR2024
MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
Tianshuo Zhou, Sen Mei, Xinze Li +5
This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval. MARVE…
cs.LG2024
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
Yumeng Song, Yu Gu, Tianyi Li +4
Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph…
cs.IR2024
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Shi Yu, Chenghao Fan, Chenyan Xiong +3
Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we prop…