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This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval.
Unifying vision-and-language tasks via text generation
Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. 2021 · 1942
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Overview of the imageclef 2008 photographic retrieval task
T Arni, M Sanderson, P Clough, and M Grubinger. 2008 · 2008
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Late fusion of heterogeneous methods for multimedia image retrieval
Hugo Jair Escalante, Carlos A Hérnadez, Luis Enrique Sucar, and Manuel Montes. 2008 · 2008
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Overview of the imageclef 2008 photographic retrieval task
M Grubinger, P Clough, A Hanbury, and H Müller. 2008 · 2008
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The probabilistic relevance framework: Bm25 and beyond
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Decoupled weight decay regularization
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Deepstyle: Multimodal search engine for fashion and interior design
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George Awad, Asad A Butt, Keith Curtis, Jonathan Fiscus, Afzal Godil, Yooyoung Lee, Andrew Delgado, Jesse Zhang, Eliot Godard, Baptiste Chocot, et al. 2021 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020 · 2020
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Multimodal few-shot learning with frozen language models
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Few-shot conversational dense retrieval
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
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Selective weak supervision for neural information retrieval
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Flamingo: a visual language model for few-shot learning
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Clueweb22: 10 billion web documents with rich information
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Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
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Linearly mapping from image to text space
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Unsupervised dense retrieval training with web anchors
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Large language models for information retrieval: A survey
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