Summary of Prompt-aware Adapter: Towards Learning Adaptive Visual Tokens For Multimodal Large Language Models, by Yue Zhang et al.
Prompt-Aware Adapter: Towards Learning Adaptive Visual Tokens for Multimodal Large Language Models
by Yue Zhang, Hehe Fan, Yi Yang
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers aim to improve communication between vision and language processing models by developing a novel adapter mechanism called prompt-aware adapters. The current approach in Multimodal Large Language Models (MLLMs) uses an adapter that converts visual inputs into tokens for Large Language Models (LLMs), but this may lead to increased cognitive load due to equal attention given to all image details. To address this issue, the authors propose a dynamic embedding of visual inputs based on the specific focus of the prompt, utilizing both global and local textual features at coarse and fine granularity levels. This approach enhances the ability of LLMs to understand and interpret visual content. The effectiveness of prompt-aware adapters is demonstrated through experiments on various visual question answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps bridge the gap between vision and language processing by creating a new type of adapter that understands what you want it to focus on. Currently, these adapters don’t really care about what’s important in an image, they just look at everything. This can make it harder for computers to understand images because they have too much information to process. To solve this problem, the authors came up with a new type of adapter that looks at the prompt and decides what parts of the image are most important. This makes it easier for computers to understand and interpret visual content. |
Keywords
» Artificial intelligence » Attention » Embedding » Prompt » Question answering
