Papers with multimodal retrieval systems

2 papers
MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline (2024.emnlp-industry)

Copied to clipboard

Challenge: Recent advances in text-video retrieval neglect the crucial user perspective, leading to discrepancies between user queries and content retrieved.
Approach: They propose a novel, training-free pipeline that leverages Large Language Models for iterative feedback learning.
Outcome: Experimental results show that MERLIN significantly outperforms existing systems in video retrieval.
M3Retrieve: Benchmarking Multimodal Retrieval for Medicine (2025.emnlp-main)

Copied to clipboard

Challenge: Strong retrieval models are increasingly important in knowledge-intensive domains.
Approach: They propose a benchmark to evaluate multimodal retrieval models in medical settings . they examine 1.2 million text documents and 164K multimodal queries .
Outcome: The proposed model spans 5 domains,16 medical fields, and 4 distinct tasks with over 1.2 Million text documents and 164K multimodal queries.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations