348 citations · 1.5k across the 43 of their papers we have counts for
3 papers · 1 filter
MIST: Multiple Instance Spatial Transformer Network
Baptiste Angles, Yuhe Jin, Simon Kornblith +2
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervisio…
LF-Net: Learning Local Features from Images
Yuki Ono, Eduard Trulls, Pascal Fua +1
We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision. To…
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector correspond…