K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations

27 Jul 2020 Haohang Xu Hongkai Xiong Guo-Jun Qi

In this paper, we propose the $K$-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances. It aims to combine the advantages of inter-instance discrimination by learning discriminative features to distinguish between different instances, as well as intra-instance variations by matching queries against the variants of augmented samples over instances... (read more)

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