Few-Shot Few-Shot Learning and the role of Spatial Attention

fully exploit the representative features from labeled support images, our method extracts rich contextual information via a multiscale feature enhancement module. This module

a) RGB-D Semantic Segmentation: Recent advances in deep learning enable the fully-supervised semantic segmen- tation on 2D images to achieve a significant performance enhancement [5,

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,

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

In several few-shot learning works, in particular metric learning, it is common to focus on the top layer of the network and learn or generate a new classifier for the novel

A multi-domain representation (introduced as “universal representation” in [2]) refers to an image representation that works equally well for a large number of visual domains.

We immediately notice that at both adaptation speeds our models are capable of surpassing the all-data model 11 in terms of accuracy on the new words, despite having seen fewer than

The present study markedly extends these earlier studies by showing that in patient-specific CMs, the CPVT RyR2-D3638A mutation causes a macromolecular complex remodeling