3 citations · 3 across the 1 of their papers we have counts for
4 papers
Few-Shot Classification with Feature Map Reconstruction Networks
Davis Wertheimer, Luming Tang, Bharath Hariharan
In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support feature…
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation
Davis Wertheimer, Omid Poursaeed, Bharath Hariharan
We aim to build image generation models that generalize to new domains from few examples. To this end, we first investigate the generalization properties of classic image generator…
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
Luming Tang, Davis Wertheimer, Bharath Hariharan
Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires…
Few-Shot Learning with Localization in Realistic Settings
Davis Wertheimer, Bharath Hariharan
Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast…