Summary of Mire: Enhancing Multimodal Queries Representation Via Fusion-free Modality Interaction For Multimodal Retrieval, by Yeong-joon Ju et al.
MIRe: Enhancing Multimodal Queries Representation via Fusion-Free Modality Interaction for Multimodal Retrieval
by Yeong-Joon Ju, Ho-Joong Kim, Seong-Whan Lee
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Multimedia (cs.MM)
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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 MIRe, a multimodal retrieval framework that enables text-based retrievers to interact with visual information without fusing textual features during alignment. The method attends to visual embeddings while avoiding text-driven signals in visual representations. A pre-training dataset is constructed by transforming question-answer pairs into extended passages. Experiments demonstrate strong performance across four multimodal retrieval benchmarks under zero-shot settings, enhancing the understanding of multimodal queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MIRe is a new way for computers to understand questions that involve images or videos. Right now, most computer systems are only good at answering text-based questions. But with MIRe, they can also understand questions that include images or videos. The system does this by looking at both the text and the image together, but it doesn’t let the text overpower the image. This helps computers understand questions better, especially when the question involves multiple types of information. |
Keywords
» Artificial intelligence » Alignment » Zero shot
