# RoRA-VLM: Robust Retrieval-Augmented Vision Language Models

arXiv: https://arxiv.org/abs/2410.08876 · HTML: https://mlsanity.com/paper/2410.08876/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 30 of its 34 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)
- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 30 of 30)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [PaLI-X: On Scaling up a Multilingual Vision and Language Model](https://mlsanity.com/paper/2305.18565/) (2023)
- [Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?](https://mlsanity.com/paper/2302.11713/) (2023)
- [UniIR: Training and Benchmarking Universal Multimodal Information Retrievers](https://mlsanity.com/paper/2311.17136/) (2023)
- [NVLM: Open Frontier-Class Multimodal LLMs](https://mlsanity.com/paper/2409.11402/) (2024)
- [Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities](https://mlsanity.com/paper/2302.11154/) (2023)
- [Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories](https://mlsanity.com/paper/2306.09224/) (2023)
- [Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs](https://mlsanity.com/paper/2404.15406/) (2024)
- [A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding](https://mlsanity.com/paper/2305.03668/) (2023)

## Built on by (most cited first, 23 of 23)

- [DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search](https://mlsanity.com/paper/2510.12801/) (2025)
- [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://mlsanity.com/paper/2503.18016/) (2025)
- [Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2411.16863/) (2024)
- [mR2AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA](https://mlsanity.com/paper/2411.15041/) (2024)
- [Knowledge-based Visual Question Answer with Multimodal Processing, Retrieval and Filtering](https://mlsanity.com/paper/2510.14605/) (2025)
- [ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2511.22715/) (2025)
- [SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking](https://mlsanity.com/paper/2506.02803/) (2025)
- [SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language Models](https://mlsanity.com/paper/2602.20901/) (2026)
- [Recurrence Meets Transformers for Universal Multimodal Retrieval](https://mlsanity.com/paper/2509.08897/) (2025)
- [AutoV: Loss-Oriented Ranking for Visual Prompt Retrieval in LVLMs](https://mlsanity.com/paper/2506.16112/) (2025)
- [CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2602.23952/) (2026)
- [Wiki-R1: Incentivizing Multimodal Reasoning for Knowledge-based VQA via Data and Sampling Curriculum](https://mlsanity.com/paper/2603.05256/) (2026)
- [CMRAG: Co-modality-based visual document retrieval and question answering](https://mlsanity.com/paper/2509.02123/) (2025)
- [StaR-KVQA: Structured Reasoning Traces for Implicit-Knowledge Visual Question Answering](https://mlsanity.com/paper/2510.06638/) (2025)
- [M3Prune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation](https://mlsanity.com/paper/2511.19969/) (2025)
- [REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment](https://mlsanity.com/paper/2602.14065/) (2026)
- [MG2-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.04969/) (2026)
- [WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2604.05818/) (2026)
- [MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems](https://mlsanity.com/paper/2605.19307/) (2026)
- [Evaluating Robustness of Vision-Language Models Under Noisy Conditions](https://mlsanity.com/paper/2509.12492/) (2025)
- [Enhancing Visual Question Answering with Multimodal LLMs via Chain-of-Question Guided Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.03790/) (2026)
- [MMAgent-R2: Learning to Rerank and Reject for Agentic mRAG](https://mlsanity.com/paper/2607.07383/) (2026)
- [Reason Before You Retrieve: Agentic Planning for Multi-modal RAG](https://mlsanity.com/paper/2607.22643/) (2026)

## Abstract (arXiv)

Current vision-language models (VLMs) still exhibit inferior performance on knowledge-intensive tasks, primarily due to the challenge of accurately encoding all the associations between visual objects and scenes to their corresponding entities and background knowledge. While retrieval augmentation methods offer an efficient way to integrate external knowledge, extending them to vision-language domain presents unique challenges in (1) precisely retrieving relevant information from external sources due to the inherent discrepancy within the multimodal queries, and (2) being resilient to the irrelevant, extraneous and noisy information contained in the retrieved multimodal knowledge snippets. In this work, we introduce RORA-VLM, a novel and robust retrieval augmentation framework specifically tailored for VLMs, with two key innovations: (1) a 2-stage retrieval process with image-anchored textual-query expansion to synergistically combine the visual and textual information in the query and retrieve the most relevant multimodal knowledge snippets; and (2) a robust retrieval augmentation method that strengthens the resilience of VLMs against irrelevant information in the retrieved multimodal knowledge by injecting adversarial noises into the retrieval-augmented training process, and filters out extraneous visual information, such as unrelated entities presented in images, via a query-oriented visual token refinement strategy. We conduct extensive experiments to validate the effectiveness and robustness of our proposed methods on three widely adopted benchmark datasets. Our results demonstrate that with a minimal amount of training instance, RORA-VLM enables the base model to achieve significant performance improvement and constantly outperform state-of-the-art retrieval-augmented VLMs on all benchmarks while also exhibiting a novel zero-shot domain transfer capability.