MiniGPT-反向设计：利用MiniGPT-4预测图像调整
MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4
AI总结：
本文通过扩展和微调MiniGPT-4，使其可应用于给定源图像、编辑后图像及可选文本描述来预测图像编辑操作与参数的反向设计任务，验证了现成VLMs在复杂多模态任务中的可扩展性。
摘要：
视觉语言模型（VLMs）近期通过与大语言模型（LLMs）集成取得了显著进展，这类同时处理图像与文本模态的模型，已在各类多模态任务中展现出学习和理解图像与文本间交互的能力。反向设计是一项复杂的视觉语言任务，可定义为：给定源图像、其编辑后的版本，以及可选的高级文本编辑描述，目标是预测编辑操作及其参数。该任务要求VLMs同时理解源图像、编辑后图像与可选文本上下文的相互作用，超出了传统视觉语言任务的范畴。本文将MiniGPT-4进行扩展并微调，以应用于反向设计任务。实验证明了现成VLMs（特别是MiniGPT-4）在反向设计等更复杂任务中的可扩展性，代码可在该仓库获取。
英文摘要：
Vision-Language Models (VLMs) have recently seen significant advancements through integrating with Large Language Models (LLMs). The VLMs, which process image and text modalities simultaneously, have demonstrated the ability to learn and understand the interaction between images and texts across various multi-modal tasks. Reverse designing, which could be defined as a complex vision-language task, aims to predict the edits and their parameters, given a source image, an edited version, and an optional high-level textual edit description. This task requires VLMs to comprehend the interplay between the source image, the edited version, and the optional textual context simultaneously, going beyond traditional vision-language tasks. In this paper, we extend and fine-tune MiniGPT-4 for the reverse designing task. Our experiments demonstrate the extensibility of off-the-shelf VLMs, specifically MiniGPT-4, for more complex tasks such as reverse designing. Code is available at this \href{https://github.com/VahidAz/MiniGPT-Reverse-Designing}{repository}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
