Summary of Mate: Meet at the Embedding — Connecting Images with Long Texts, by Young Kyun Jang et al.
MATE: Meet At The Embedding – Connecting Images with Long Texts
by Young Kyun Jang, Junmo Kang, Yong Jae Lee, Donghyun Kim
First submitted to arxiv on: 26 Jun 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 proposed Meet At The Embedding (MATE) approach combines Vision Language Models (VLMs) and Large Language Models (LLMs) to align images with longer text inputs. By replacing the VLM’s text encoder with a pretrained LLM-based encoder, MATE excels in understanding lengthy captions or documents. A projection module is trained to bridge the gap between VLM and LLM embeddings. The approach is evaluated on two new cross-modal retrieval benchmarks for connecting images with long texts. Experimental results demonstrate MATE’s effectiveness in uncovering diverse semantic relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MATE is a new way to connect images with long texts, like captions or documents. It uses special models called Vision Language Models (VLMs) and Large Language Models (LLMs). These models are great at understanding short text, but not long text. MATE solves this problem by replacing the VLM’s text understanding part with a LLM-based part that is super good at understanding long text. To make it all work together, MATE uses a special module that helps align the two types of embeddings. It then tests its idea on two new ways to measure how well it works. |
Keywords
» Artificial intelligence » Embedding » Encoder
