2 citations · 3 across the 2 of their papers we have counts for
5 papers
Self-supervised Segmentation via Background Inpainting
Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they h…
Self-supervised Training of Proposal-based Segmentation via Background Prediction
Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained…
Neural Scene Decomposition for Multi-Person Motion Capture
Helge Rhodin, Victor Constantin, Isinsu Katircioglu +2
Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networ…
Learning Monocular 3D Human Pose Estimation from Multi-view Images
Helge Rhodin, Jörg Spörri, Isinsu Katircioglu +5
Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves…
Structured Prediction of 3D Human Pose with Deep Neural Networks
Bugra Tekin, Isinsu Katircioglu, Mathieu Salzmann +2
Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which igno…