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In this paper, we introduce ML-Decoder, a new attention-based classification head.
Multitask learning
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Large-scale multi-label learning with missing labels, 2013
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Hao Yang, Joey Tianyi Zhou, Yu Zhang, Bin-Bin Gao, Jianxin Wu, and Jianfei Cai · 2016
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Fast zero-shot image tagging
Yang Zhang, Boqing Gong, and Mubarak Shah · 2016
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Chen-Yu Lee, Patrick Gallagher, and Zhuowen Tu · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Yongqin Xian, Bernt Schiele, and Zeynep Akata · 2017
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Arna Ghosh, Biswarup Bhattacharya, and Somnath Basu Roy Chowdhury · 2018
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A survey of zero-shot learning: Settings, methods, and applications
Wei Wang, Vincent W Zheng, Han Yu, and Chunyan Miao · 2019
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Asymmetric loss for multi-label classification
Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, and Lihi Zelnik-Manor · 2020
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Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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A shared multi-attention framework for multi-label zero-shot learning
Dat Huynh and Ehsan Elhamifar · 2020
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Bi-modal learning with channel-wise attention for multi-label image classification
Peng Li, Peng Chen, Yonghong Xie, and Dezheng Zhang · 2020
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Multi-label classification with partial annotations using class-aware selective loss, 2021
Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman, Avi Ben-Cohen, Nadav Zamir, Asaf Noy, and Lihi Zelnik-Manor · 2021
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Semantic diversity learning for zero-shot multi-label classification
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