CVPR 2019 Open Access Repository

Dense Classification and Implanting for Few-Shot Learning Abstract Few-shot learning for deep neural networks is a highly challenging and key problem in many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) impl

Date: 2020/08/15 12:52