Summary of Mm-embed: Universal Multimodal Retrieval with Multimodal Llms, by Sheng-chieh Lin et al.
MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs
by Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoeybi, Jimmy Lin, Bryan Catanzaro, Wei Ping
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 This paper introduces techniques for advancing information retrieval with multimodal large language models (MLLMs), enabling a broader search scenario where multiple modalities and diverse retrieval tasks are accommodated. The authors study fine-tuning an MLLM as a bi-encoder retriever on 10 datasets with 16 retrieval tasks, achieving state-of-the-art performance on the multimodal retrieval benchmark M-BEIR and surpassing the state-of-the-art text retrieval model, NV-Embed-v1, on the MTEB retrieval benchmark. The authors also propose modality-aware hard negative mining to mitigate the modality bias exhibited by MLLM retrievers and continuously fine-tuning the universal multimodal retriever to enhance its text retrieval capability while preserving multimodal retrieval capability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes information retrieval better by using language models that can understand multiple types of data, like text and images. The authors tested these models on many different tasks and showed they work well. They also found a way to make the models better at understanding complex queries that combine multiple modalities. This could be useful in many applications, such as searching for information online or organizing digital files. |
Keywords
» Artificial intelligence » Encoder » Fine tuning
