Researchers have developed ColNanoVDR, a novel framework for document-free query distillation in multi-vector visual document retrieval. This method allows smaller student models to efficiently query large teacher models without needing to process or cache extensive page data. The framework utilizes an Optimal Transport with Learned Weights (OTW) objective to align student query tokens with teacher embeddings, achieving approximately 95% of the teacher's performance while significantly speeding up query encoding. AI
IMPACT Accelerates visual document retrieval by enabling smaller models to query large teacher models more efficiently.
RANK_REASON The cluster describes a new research paper detailing a novel framework for improving visual document retrieval.
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXiv
- CatalyzeX
- ColNanoVDR
- DagsHub
- Hugging Face
- NanoVDR
- nDCG@5
- optimal transport
- Ryenhails
- ViDoRe v1-v3
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