Re-ranking the Context for Multimodal Retrieval Augmented Generation

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Retrieval-augmented generation for knowledge-intensive nlp tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, et al · 2020
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Earlier work this paper cites.
Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval
N. Messina, M. Stefanini, M. Cornia, L. Baraldi, F. Falchi, G. Amato, and R. Cucchiara · 2022
Earlier work this paper cites.
Gpt-4 technical report
Original
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
Earlier work this paper cites.

Learnable pillar-based re-ranking for image-text retrieval
L. Qu, M. Liu, W. Wang, Z. Zheng, L. Nie, and T. S. Chua · 2023
Cited alongside, same era.
Ai-generated images introduce invisible relevance bias to text-image retrieval
Original
S. Xu, D. Hou, L. Pang, J. Deng, J. Xu, H. Shen, and X. Cheng · 2023
Cited alongside, same era.
Mllm is a strong reranker: Advancing multimodal retrieval-augmented generation via knowledge-enhanced reranking and noise-injected training
Original
Z. Chen, C. Xu, Y. Qi, and J. Guo · 2024
Cited alongside, same era.
Toward automatic relevance judgment using vision–language models for image–text retrieval evaluation
Original
J. H. Yang and J. Lin · 2024
Cited alongside, same era.

Visual instruction tuning
H. Liu, C. Li, Q. Wu, and Y. J. Lee · 2024
Later among the works it cites.
Vila: On pre-training for visual language models
J. Lin, H. Yin, W. Ping, P. Molchanov, M. Shoeybi, and S. Han · 2024
Later among the works it cites.
Rag-check: Evaluating multimodal retrieval augmented generation performance
Original
M. Mortaheb, M. A. Khojastepour, S. T. Chakradhar, and S Ulukus · 2025
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Re-ranking the Context for Multimodal Retrieval Augmented Generation · Around

Retrieval-augmented generation for knowledge-intensive nlp tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, et al · 2020
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Earlier work this paper cites.
Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval
N. Messina, M. Stefanini, M. Cornia, L. Baraldi, F. Falchi, G. Amato, and R. Cucchiara · 2022
Earlier work this paper cites.
Gpt-4 technical report
Original
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
Earlier work this paper cites.

Learnable pillar-based re-ranking for image-text retrieval
L. Qu, M. Liu, W. Wang, Z. Zheng, L. Nie, and T. S. Chua · 2023
Cited alongside, same era.
Ai-generated images introduce invisible relevance bias to text-image retrieval
Original
S. Xu, D. Hou, L. Pang, J. Deng, J. Xu, H. Shen, and X. Cheng · 2023
Cited alongside, same era.
Mllm is a strong reranker: Advancing multimodal retrieval-augmented generation via knowledge-enhanced reranking and noise-injected training
Original
Z. Chen, C. Xu, Y. Qi, and J. Guo · 2024
Cited alongside, same era.
Toward automatic relevance judgment using vision–language models for image–text retrieval evaluation
Original
J. H. Yang and J. Lin · 2024
Cited alongside, same era.

Visual instruction tuning
H. Liu, C. Li, Q. Wu, and Y. J. Lee · 2024
Later among the works it cites.
Vila: On pre-training for visual language models
J. Lin, H. Yin, W. Ping, P. Molchanov, M. Shoeybi, and S. Han · 2024
Later among the works it cites.
Rag-check: Evaluating multimodal retrieval augmented generation performance
Original
M. Mortaheb, M. A. Khojastepour, S. T. Chakradhar, and S Ulukus · 2025
Closest in time.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…