194 citations · 649 across the 42 of their papers we have counts for
6 papers · 1 filter
One-Shot Weakly Supervised Video Object Segmentation
Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin +2
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks. Recent work has shown to p…
DECoVaC: Design of Experiments with Controlled Variability Components
Thomas Boquet, Laure Delisle, Denis Kochetkov +4
Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance. We…
CLAREL: Classification via retrieval loss for zero-shot learning
Boris N. Oreshkin, Negar Rostamzadeh, Pedro O. Pinheiro +1
We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key n…
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados +1
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links an…
Adaptive Masked Proxies for Few-Shot Segmentation
Mennatullah Siam, Boris Oreshkin, Martin Jagersand
Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method th…
Adaptive Cross-Modal Few-Shot Learning
Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin +1
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance m…