MAR: Matching-Augmented Reasoning for Enhancing Visual-based Entity Question Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal large language models (MLLMs) struggle with visual-based entity questions (VEQA) MLLM can identify A, but may refrain from answering due to privacy concerns. |
| Approach: | They propose a method that uses vector representations to analyze visual-based entity questions (VEQA) they use visual cues and textual information to integrate visual cus and visual information . |
| Outcome: | The proposed method significantly improves visual-based entity question answering (VEQA) it can identify faces, names, and alignments within visual objects, and then derive the answer over this matching graph. |
Similar Papers
Large Language Models Know What is Key Visual Entity: An LLM-assisted Multimodal Retrieval for VQA (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing visual language models struggle to capture longtail knowledge in the real world due to redundant visual information. |
| Approach: | They propose a method leveraging the reasoning capability of a large language model to identify key visual entities. |
| Outcome: | The proposed method outperforms other strong visual language model-based systems in two knowledge-intensive VQA benchmarks and performs comparably to models with 1-2 orders larger parameters. |
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. |
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM (2024.findings-emnlp)
Copied to clipboard
Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul Crook, Yifan Xu, Babak Damavandi, Xin Dong, Christos Faloutsos, Lei Li, Seungwhan Moon
| Challenge: | Vision-extended LLMs have made significant strides in VQA, but they still encounter significant difficulties in handling queries involving long-tail entities. |
| Approach: | They propose a benchmark to test models' ability to identify entities and provide detailed, entity-specific knowledge by combining 10 images and 10 knowledge-intensive QA pairs. |
| Outcome: | The proposed model outperforms existing methods on the SnapNTell dataset, achieving a 66.5% improvement in the BELURT score. |
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)
Copied to clipboard
| Challenge: | Current approaches generate visual markers for all questions, generating excessive visual markers. |
| Approach: | They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning . |
| Outcome: | The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME. |
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. |
Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
Copied to clipboard
Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
| Approach: | They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs. |
| Outcome: | The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content. |
M3-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge-based VQA benchmarks focus on coarse-grained categories and simple reasoning over single entities. |
| Approach: | They propose a knowledge-based Visual Question Answering benchmark to enhance multimodality evaluation. |
| Outcome: | The proposed benchmark improves evaluation of multimodal large language models in fine-grained multimodal entity understanding and complex multihop reasoning. |
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)
Copied to clipboard
| Challenge: | Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. |
| Approach: | They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer. |
| Outcome: | The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets. |
Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization (2025.emnlp-main)
Copied to clipboard
Shuo Xing, Peiran Li, Yuping Wang, Ruizheng Bai, Yueqi Wang, Chan-Wei Hu, Chengxuan Qian, Huaxiu Yao, Zhengzhong Tu
| Challenge: | emergence of large Vision Language Models (VLMs) has broadened the capabilities of single-modal Large Language Model (LLM) but VLMs are prone to significant hallucinations, especially in the form of cross-modal inconsistencies. |
| Approach: | They propose a new alignment framework that leverages image retrieval to integrate both textual and visual preference signals. |
| Outcome: | The proposed framework mitigates hallucinations more effectively than previous methods . it maintains robustness and scalability across a wide range of VLM sizes and architectures . |
Visual–Linguistic Abductive Reasoning with LLMs for Knowledge-based Visual Question Answering (2026.findings-eacl)
Copied to clipboard
| Challenge: | Recent efforts to leverage large language models for reasoning focus on visual perception and language reasoning as separate processes. |
| Approach: | They propose a method that integrates visual and linguistic modalities into interpretable abductive reasoning chains. |
| Outcome: | The proposed method improves performance on AOKVQA, OKVQA and GQA by 2.31% . it uses fuzzy scoring to select the most coherent combination, enabling unified reasoning . |