The article discusses using pretrained foundation models to tackle Visual Question Answering (VQA) without the need for further training. Large language models (LLMs) have shown success in natural language processing tasks and can adapt to different tasks through zero-shot or few-shot settings. Researchers have been exploring how to utilize LLMs for VQA, but many methods require additional training which is computationally expensive and requires large image-text datasets. The authors propose a method that combines pretrained LLMs and other foundation models without additional training to solve the VQA problem. The approach involves using natural language to represent images so that the LLM can understand them. Different decoding strategies are explored for generating textual representations of images, and their performance is evaluated on the VQAv2 dataset. This method aims to leverage the capabilities of LLMs without the need for extensive training, offering a more efficient solution for VQA tasks.
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Source link: https://medium.com/@monocosmo77/latest-research-on-vqa-part1-machine-learning-2024-67724fb40861
#CuttingEdgeResearch VQA in Machine Learning: Latest Findings and Trends #VQAResearch
