Papers with M2KR
WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering (2026.findings-acl)
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| Challenge: | Existing methods for Knowledge-Based Visual Question Answering rely on images as the retrieval key, and often overlook or misplace the role of Vision-Language Models (VLMs) |
| Approach: | They propose a multi-modal RAG framework that assigns VLMs two specialized agents: a Refiner and an Inspector. |
| Outcome: | Experiments on EVQA, InfoSeek, and M2KR show that the proposed framework achieves state-of-the-art performance with significant improvements in both retrieval accuracy and answer quality. |
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers (2024.acl-long)
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| Challenge: | Large Multimodal Models excel in natural language and visual understanding but are challenged by challenging tasks such as Knowledge-based Visual Question Answering (KB-VQA). |
| Approach: | They propose a framework for training Large Multimodal Models (LMMs) to perform KB-VQA tasks. |
| Outcome: | The proposed framework is used to train and evaluate multi-modal retrievers. |