Unifying Image Processing as Visual Prompting Question Answering
Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Our methodology offers a universal and adaptive solution to general image processing. While PromptGIP has demonstrated a certain degree of out-of-domain task generalization capability, further research is expected to fully explore its more powerful emergent generalization.
Code (0)
등록된 구현이 없습니다.
Tasks
Image EnhancementImage RestorationIn-Context LearningQuestion AnsweringSentenceVisual PromptingSimilar Papers 제목 키워드 기반
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
In this paper, we explore effective prompting techniques to enhance zero- and few-shot Visual Question Answering (VQA) performance in contemporary Vision-Language Models (VLMs). Central to our investigation is the role o…
Image CaptioningQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Chain of Thought Prompt Tuning in Vision Language Models
Language-Image Pre-training has demonstrated promising results on zero-shot and few-shot downstream tasks by prompting visual models with natural language prompts. However, most recent studies only use a single prompt fo…
Domain Generalizationimage-classificationImage ClassificationLanguage Modeling+4Image-of-Thought Prompting for Visual Reasoning Refinement in Multimodal Large Language Models
Recent advancements in Chain-of-Thought (CoT) and related rationale-based works have significantly improved the performance of Large Language Models (LLMs) in complex reasoning tasks. With the evolution of Multimodal Lar…
Multimodal ReasoningVisual Question AnsweringVisual ReasoningQG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a diminished capability to effectively reason ov…
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM
Recent advancements in multimodal large language models (MLLMs) have broadened the scope of vision-language tasks, excelling in applications like image captioning and interactive question-answering. However, these models…
Image CaptioningObject RecognitionQuestion AnsweringVisual Prompting