2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
Haohang Huang, Xuan Lu, Mingyi Su +9
Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space. However, existing methods and benchmarks remain…
VISA: Retrieval Augmented Generation with Visual Source Attribution
Xueguang Ma, Shengyao Zhuang, Bevan Koopman +3
Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems. However, existing approaches in RAG primarily link…
Unifying Multimodal Retrieval via Document Screenshot Embedding
Xueguang Ma, Sheng-Chieh Lin, Minghan Li +2
In the real world, documents are organized in different formats and varied modalities. Traditional retrieval pipelines require tailored document parsing techniques and content extr…
UniRAG: Universal Retrieval Augmentation for Large Vision Language Models
Sahel Sharifymoghaddam, Shivani Upadhyay, Wenhu Chen +1
Recently, Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding (e.g., image captioning or visual question answering…