Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

This survey provides a comprehensive review of retrieval-augmented generation (RAG) techniques applied to computer vision, covering visual understanding, visual generation, and embodied AI applications. It synthesizes the current landscape and proposes future research directions for RAG-enhanced vision models.

Problem Statement

Vision models traditionally rely solely on internalized parametric knowledge, which limits their ability to access up-to-date, authoritative, or domain-specific information at inference time. RAG has proven transformative in NLP but its systematic application to CV tasks remains fragmented and underexplored. This survey addresses the lack of a unified framework for understanding how retrieval-augmented strategies can improve both the understanding and generation capabilities of vision systems.

Key Novelty

First comprehensive survey systematically categorizing RAG techniques in computer vision across both visual understanding (image recognition, medical report generation, multimodal QA) and visual generation (image, video, 3D) tasks
Novel taxonomy covering RAG integration in embodied AI, including planning, task execution, multimodal perception, and interaction in specialized domains
Identification of key limitations in current RAG-for-vision approaches and a forward-looking research agenda to guide future development of this emerging field

Evaluation Highlights

Qualitative synthesis across diverse CV benchmarks showing RAG-augmented models consistently outperform closed-book counterparts in knowledge-intensive visual tasks such as medical report generation and multimodal QA
Survey demonstrates RAG reduces hallucination and improves factual grounding in visual generation tasks (image, video, 3D) by anchoring outputs to retrieved external references

Breakthrough Assessment

5/10 This is a solid and timely survey contribution that consolidates a rapidly growing area, but as a survey paper it does not introduce a novel algorithm or achieve new state-of-the-art results; its value lies in organizing and framing the field rather than a technical breakthrough.

Methodology

Systematic literature review of RAG techniques adapted for CV, categorized into visual understanding tasks (recognition, captioning, VQA, medical imaging) and visual generation tasks (image, video, 3D synthesis)
Analysis of how retrieval mechanisms (dense retrieval, cross-modal retrieval, knowledge graph retrieval) are integrated with vision-language models and generative models to augment outputs with external knowledge
Critical evaluation of current limitations (retrieval quality, modality alignment, scalability) and synthesis of open research challenges and future directions for RAG in embodied AI and specialized vision domains

System Components

Retrieval Module

Fetches relevant external knowledge (images, text, structured data) from knowledge bases or databases using dense or sparse retrieval strategies conditioned on visual or multimodal queries

Visual Understanding with RAG

Applies retrieved context to tasks like image recognition, visual question answering, and medical report generation to ground predictions in external authoritative knowledge

Visual Generation with RAG

Conditions generative models for image, video, and 3D content creation on retrieved references to improve fidelity, diversity, and factual accuracy

Embodied AI RAG Integration

Augments agent planning, task execution, and multimodal perception with retrieved procedural and domain knowledge to enable more capable real-world interaction

Taxonomy & Future Outlook

Provides a structured classification of the RAG-for-vision landscape and highlights key open problems including retrieval-generation alignment, scalability, and cross-modal grounding

Results

Task/Domain	Closed-Book Baseline	RAG-Augmented	Delta
Medical Report Generation	Limited factual accuracy	Improved clinical grounding	Qualitative improvement
Multimodal QA (e.g., OK-VQA)	Relies on parametric knowledge only	Retrieval boosts knowledge coverage	Consistent accuracy gains reported
Image Generation Fidelity	Single-model prior	Reference-anchored generation	Improved visual consistency
3D Content Generation	No external reference	Retrieved 3D assets guide synthesis	Better geometric fidelity
Embodied Planning	Static model knowledge	Dynamic retrieval of task plans	Improved task success rates

Key Takeaways

RAG is not just an NLP technique — integrating retrieval into vision pipelines is a practical strategy for reducing hallucinations and improving factual grounding in tasks like medical imaging, VQA, and captioning without retraining the base model
Cross-modal retrieval (using image queries to retrieve text knowledge or vice versa) is a critical design choice; practitioners should carefully align retrieval indices with the modality and granularity of their target task
Embodied AI is an emerging and high-potential application area for RAG in vision, where retrieving procedural knowledge at inference time can significantly improve agent adaptability in novel environments

Abstract

Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs) by enabling access to external, reliable, and up-to-date knowledge sources. In the context of AI-Generated Content (AIGC), RAG has proven invaluable by augmenting model outputs with supplementary, relevant information, thus improving their quality. Recently, the potential of RAG has extended beyond natural language processing, with emerging methods integrating retrieval-augmented strategies into the computer vision (CV) domain. These approaches aim to address the limitations of relying solely on internal model knowledge by incorporating authoritative external knowledge bases, thereby improving both the understanding and generation capabilities of vision models. This survey provides a comprehensive review of the current state of retrieval-augmented techniques in CV, focusing on two main areas: (I) visual understanding and (II) visual generation. In the realm of visual understanding, we systematically review tasks ranging from basic image recognition to complex applications such as medical report generation and multimodal question answering. For visual content generation, we examine the application of RAG in tasks related to image, video, and 3D generation. Furthermore, we explore recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains. Given that the integration of retrieval-augmented techniques in CV is still in its early stages, we also highlight the key limitations of current approaches and propose future research directions to drive the development of this promising area.