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Solving multi-label recognition (MLR) for images in the low-label regime is a challenging task with many real-world applications.
Multi-label classification: An overview
Grigorios Tsoumakas and Ioannis Katakis · 2007
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Yining Li, Chen Huang, Chen Change Loy, and Xiaoou Tang · 2016
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Feng Liu, Tao Xiang, Timothy M Hospedales, Wankou Yang, and Changyin Sun · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
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Attention is all you need
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Multi-label zero-shot learning with structured knowledge graphs
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Orderless recurrent models for multi-label classification
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Semantic diversity learning for zero-shot multi-label classification
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Clip-adapter: Better vision-language models with feature adapters
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Scaling up visual and vision-language representation learning with noisy text supervision
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Deep multiple instance learning for zero-shot image tagging
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Partial multi-label learning
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Structured semantic transfer for multi-label recognition with partial labels
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