demonstrated as an effective approach for few-shot learning The episodes used in training simulate the settings in test Each episode is formed by randomly
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[PDF] Few-Shot Learning With Localization in Realistic Settings
Localization improves accuracy by 6 points, more so when objects occupy under 40 of the image Even after localizing the object, the learner may need to look for subtle distinctions between concepts Existing few-shot methods rely on the learning process alone to build informative feature representations
Few-Shot Learning With Localization in Realistic Settings
We adapt bilinear pooling [26] to the few- shot setting as a truly parameter-free expansion, which no longer risks overfitting to small datasets Localization: A close
[PDF] Advances in Few-shot Learning - UCF EECS
Few-Shot Learning with Localization in Realistic Settings CVPR 2019 [31] Garcia, Victor, and Joan Bruna Few-shot learning with graph neural networks
[PDF] Cross Attention Network for Few-shot Classification - NeurIPS
demonstrated as an effective approach for few-shot learning The episodes used in training simulate the settings in test Each episode is formed by randomly
[PDF] Transductive Information Maximization For Few-Shot Learning
Following standard transductive few-shot settings, our compre- Few-shot learning with localization in realistic settings In IEEE Conference on Computer
[PDF] Paper - PRELIMINARY VERSION DO NOT CITE
few-shot learning using deep convolutional neural networks (CNNs) tion in the few-shot setting with Localization in Realistic Settings URL http://arxiv org/
[PDF] Paper - PRELIMINARY VERSION DO NOT CITE
Few-shot learning (FSL) aims to recognize target classes by Inspired by the few-shot learning ability of humans, there with localization in realistic settings
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