Summary of Lcv2: An Efficient Pretraining-free Framework For Grounded Visual Question Answering, by Yuhan Chen et al.
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering
by Yuhan Chen, Lumei Su, Lihua Chen, Zhiwei Lin
First submitted to arxiv on: 29 Jan 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 The LCV2 modular method is a novel approach for Grounded Visual Question Answering in the vision-language domain. It uses a frozen large language model as an intermediate mediator between off-the-shelf VQA and visual grounding models, transforming textual information based on designed prompts. The integrated plug-and-play framework can be deployed with low computational resources and allows application with various pre-trained models. Experimental results on benchmark datasets GQA, CLEVR, and VizWiz-VQA-Grounding demonstrate the competitiveness of LCV2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to answer questions that combine text and images. It’s like having a translator between two different languages – one for text and one for pictures. This translator is called LCV2, which uses a pre-trained language model to help VQA models understand visual information better. The best part is that it can work with many different state-of-the-art models without needing special training. This makes it useful for answering questions in real-world applications where resources are limited. |
