Papers with multi-modal framework
Seeing Beyond: Enhancing Visual Question Answering with Multi-Modal Retrieval (2025.coling-industry)
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| 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. |
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification (2025.acl-industry)
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Praphul Singh, Charlotte Dzialo, Jangwon Kim, Sumana Srivatsa, Irfan Bulu, Sri Gadde, Krishnaram Kenthapadi
| Challenge: | Existing de-identification methods suffer from recall errors, limited generalization, and inefficiencies, limiting their real-world applicability. |
| Approach: | They propose a multi-modal framework for de-identifying electronic health records using a retrieval-based entity relexicalization approach. |
| Outcome: | The proposed framework achieves competitive performance while optimizing token usage to reduce LLM costs. |
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding (2023.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) are capable of understanding multi-modal content, but textonly human-computer interaction is not sufficient for many application scenarios. |
| Approach: | They propose a video-to-text generation task and a multi-modal framework that bootstraps cross-modal training from frozen pre-trained visual & audio encoders and frozen LLMs. |
| Outcome: | The proposed framework can understand both visual and auditory content in video and generate meaningful responses grounded in the visual and audio information presented in the videos. |
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim? (2023.eacl-main)
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Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty
| Challenge: | A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. |
| Approach: | They propose to use a memes dataset on US Politics and Covid-19 memes to characterize the role of harmful entities in memes. |
| Outcome: | The proposed model improves 4% over baseline and 1% over competing models. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |