Vidore V-2

The 'Vidore V2' dataset/benchmark is used to evaluate the performance of retrieval systems by assessing the effectiveness of multi-vector embeddings in document retrieval tasks.

Papers using Vidore V-2 (6)

Reinpool: Reinforcement Learning Pooling Multi-vector Embeddings For Retrieval System2026Evo-Retriever: LLM-Guided Curriculum Evolution with Viewpoint-Pathway Collaboration for Multimodal Document Retrieval2026Structural Anchor Pruning: Training-Free Multi-Vector Compression for Visual Document Retrieval2026MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings2025Colmate: Contrastive Late Interaction And Masked Text For Multimodal Document Retrieval2025Llama Nemoretriever Colembed: Top-performing Text-image Retrieval Model2025