Summary of Merlin: Multimodal Embedding Refinement Via Llm-based Iterative Navigation For Text-video Retrieval-rerank Pipeline, by Donghoon Han et al.
MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline
by Donghoon Han, Eunhwan Park, Gisang Lee, Adam Lee, Nojun Kwak
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MERLIN, a novel pipeline for retrieving relevant videos from large collections. Traditional text-video retrieval methods often neglect user perspectives, leading to discrepancies between queries and content retrieved. To address this, MERLIN leverages Large Language Models (LLMs) for iterative feedback learning. The system refines query embeddings from a user perspective through a dynamic question answering process. Experimental results on datasets like MSR-VTT, MSVD, and ActivityNet demonstrate that MERLIN substantially improves Recall@1, outperforming existing systems. By integrating LLMs into multimodal retrieval systems, MERLIN enables more responsive and context-aware multimedia retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem: finding the right videos from millions of options on the internet. Right now, most video search engines don’t understand what we really want to see. They just show us random videos that match our search terms. The authors of this paper created a new system called MERLIN that helps search engines understand what we’re looking for and find the perfect videos for us. MERLIN uses special language models to learn how to ask better questions and get better answers. This makes searching for videos much more accurate and helpful. |
Keywords
» Artificial intelligence » Question answering » Recall
