MM-Reasoner: A Multi-Modal Knowledge-Aware Framework for Knowledge-Based Visual Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent knowledge-based visual question answering approaches miss visual information captured by captions and cannot fully utilize the visual information required to answer the question. |
| Approach: | They propose a framework that extracts visual information from an image and prompts an LLM to extract query-specific knowledge from the extracted textual information. |
| Outcome: | Empirical results show that MM-Reasoner achieves state-of-the-art performance on several KVQA datasets. |
Similar Papers
Modality-Aware Integration with Large Language Models for Knowledge-Based Visual Question Answering (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to integrate multimodal knowledge in a modality-agnostic manner can be sub-optimal. |
| Approach: | They propose a modality-aware integration with large language models (LLMs) that leverages multimodal knowledge for both image understanding and knowledge reasoning. |
| Outcome: | The proposed model is able to bridge a tight inter-modal exchange while preserving insightful intra-modal learning. |
WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering (2026.findings-acl)
Copied to clipboard
| 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. |
Seeing Beyond: Enhancing Visual Question Answering with Multi-Modal Retrieval (2025.coling-industry)
Copied to clipboard
| Challenge: | Multi-modal Large language models still suffer from model hallucination and lack of specific knowledge when answering challenging questions. |
| Approach: | They propose to use a multi-modal retrieval augmented generation method to integrate knowledge from all modalities into a model to enable alignment between query and knowledge. |
| Outcome: | The proposed method achieves significant performance improvement on the VQA dataset. |
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have focused on assessing the model’s overall accuracy without evaluating it on different reasoning cases. |
| Approach: | They propose a novel idea to identify and improve multi-modal multi-hop reasoning in VQA by using two new language prompts to find a reasoning path to reach its answer. |
| Outcome: | The proposed model improves multi-modal multi-hop reasoning in visual question answering (VQA) it finds that the proposed model is easy to answer, simply demanding “single-hop” reasoning, whereas only a few questions require “multi-hop.” |
Visual Text Matters: Improving Text-KVQA with Visual Text Entity Knowledge-aware Large Multimodal Assistant (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge-aware text-based visual question answering methods are based on textual entities in images. |
| Approach: | They propose a visual text entity linking module that harnesses a state-of-the-art visual text recognition engine and the power of a large multimodal model to perform visual text-entity linking. |
| Outcome: | The proposed approach surpasses the previous best approach by 23.3% on an absolute scale and establishes a new state of the art. |
KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Visual Question Answering lack synergistic potential of scene graphs and scene graph. |
| Approach: | They propose a retrieval-and-fusion pipeline that fuses scene graphs and commonsense graphs to enable multi-modal reasoning. |
| Outcome: | Experiments on FVQA 2.0+ and MVQA benchmarks show that KG-ViP outperforms existing methods. |
EchoSight: Advancing Visual-Language Models with Wiki Knowledge (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge-based visual question answering systems struggle with these tasks due to limited integration of external knowledge. |
| Approach: | They propose a framework that enables large language models to answer visual questions requiring encyclopedic knowledge. |
| Outcome: | The proposed framework improves retrieval outcomes and accuracy of knowledge-based visual question answering tasks. |
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment (2024.acl-long)
Copied to clipboard
| Challenge: | Recent Large Multimodal Models (LMMs) focus on visual knowledge-dimension alignment, but ignore visual knowledge. |
| Approach: | They propose a cognitive visual-language mapper that integrates visual-linguistic knowledge alignment with a fine-grained knowledge Adapter. |
| Outcome: | The proposed model significantly improves LMMs on knowledge-based visual question answering (VQA) it also improves the performance of other models, including GPT-4V and Gemini-Pro. |
Unifying Text, Tables, and Images for Multimodal Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to multimodal question answering rely on single-modal or bi-modal models, which limit their ability to integrate information across all modalities. |
| Approach: | They propose a framework that unifies three different input modalities into a text-to-text format by employing position-enhanced table linearization and diversified image captioning techniques. |
| Outcome: | The proposed framework unifies three input modalities into a text-to-text format using position-enhanced table linearization and diversified image captioning techniques. |
ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom (2025.emnlp-main)
Copied to clipboard
Jingqi Zhou, Sheng Wang, Jingwei Dong, Kai Liu, Lei Li, Jiahui Gao, Jiyue Jiang, Lingpeng Kong, Chuan Wu
| Challenge: | Large vision-language models often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation. |
| Approach: | They propose a visual reasoning framework that decouples vision-reasoning capabilities and multi-run proactive perception. |
| Outcome: | The proposed framework outperforms existing models on benchmarks for open-source and closed-source models with 13.2% performance gain. |