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We propose Fuse Local and Global Semantics in Representation Learning (FLAGS) to generate richer representations.
Dimensionality reduction by learning an invariant mapping
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness, 2019
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
Momentum contrast for unsupervised visual representation learning, 2020
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases, 2020
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Can semantic labels assist self-supervised visual representation learning?, 2020
L. Wei, L. Xie, J. He, J. Chang, X. Zhang, W. Zhou, H. Li, and Q. Tian · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
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Detectron2
Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick · 2019
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End-to-end object detection with transformers, 2020
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
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A simple framework for contrastive learning of visual representations, 2020
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Improved baselines with momentum contrastive learning, 2020
X. Chen, H. Fan, R. Girshick, and K. He · 2020
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P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2021
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Dense contrastive learning for self-supervised visual pre-training, 2021
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What should not be contrastive in contrastive learning, 2021
T. Xiao, X. Wang, A. A. Efros, and T. Darrell · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers, 2021
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. S. Torr, and L. Zhang · 2021
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