no code implementations • 13 Jun 2020 • Omid Hosseini Jafari, Carsten Rother
There are a variety of approaches to obtain a vast receptive field with convolutional neural networks (CNNs), such as pooling or striding convolutions.
2 code implementations • 17 Apr 2018 • Weihao Li, Omid Hosseini jafari, Carsten Rother
This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images.
no code implementations • 5 Dec 2017 • Omid Hosseini Jafari, Siva Karthik Mustikovela, Karl Pertsch, Eric Brachmann, Carsten Rother
We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.
no code implementations • ICCV 2017 • Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, Andreas Geiger
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e. g., at texture-less or reflective surfaces.
no code implementations • 26 Feb 2017 • Omid Hosseini Jafari, Oliver Groth, Alexander Kirillov, Michael Ying Yang, Carsten Rother
Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling.
no code implementations • 3 Oct 2016 • Omid Hosseini jafari, Michael Ying Yang
We show that our method outperforms the state-of-the-art approaches.