MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4

[2406.00971] MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4 Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2406.00971 (cs) [Submitted on 3 Jun 2024 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4 Authors: Vahid Azizi , Fatemeh Koochaki View a PDF of the paper titled MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4, by Vahid Azizi and 1 other authors View PDF HTML (experimental) Abstract: 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{ this https URL }{repository}. Comments: 8 pages, 7 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2406.00971 [cs.CV] (or arXiv:2406.00971v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2406.00971 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Vahid Azizi [ view email ] [v1] Mon, 3 Jun 2024 03:59:29 UTC (5,265 KB) [v2] Fri, 30 Aug 2024 01:50:37 UTC (5,564 KB) [v3] Sat, 15 Aug 2026 05:07:11 UTC (4,558 KB) Full-text links: Access Paper: View a PDF of the paper titled MiniGPT-Reverse-Designing: Predicting Image Adjustments Utilizing MiniGPT-4, by Vahid Azizi and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• FrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion#614958
• FrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion#613991
• Unsupervised visual structuring of polished ornamental granites using self-supervised vision transformers#12038
• Unsupervised visual structuring of polished ornamental granites using self-supervised vision transformers#12037
• How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks#189297