18 citations · 19 across the 3 of their papers we have counts for
4 papers · 1 filter
Local Propagation for Few-Shot Learning
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image…
Few-Shot Few-Shot Learning and the role of Spatial Attention
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representat…
Dense Classification and Implanting for Few-Shot Learning
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard +1
Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a…
Deep multi-scale architectures for monocular depth estimation
Michel Moukari, Sylvaine Picard, Loic Simon +1
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images. More precisely, the paper investigates four different deep CN…