831 citations · 994 across the 15 of their papers we have counts for
7 papers · 1 filter
ODFNet: Using orientation distribution functions to characterize 3D point clouds
Yusuf H. Sahin, Alican Mertan, Gozde Unal
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neu…
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks
Gulcin Baykal, Furkan Ozcelik, Gozde Unal
Generative Adversarial Networks (GANs) have become the most used networks towards solving the problem of image generation. Self-supervised GANs are later proposed to avoid the cata…
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation
Alican Mertan, Yusuf Huseyin Sahin, Damien Jade Duff +1
We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation…
Relative Depth Estimation as a Ranking Problem
Alican Mertan, Damien Jade Duff, Gozde Unal
We present a formulation of the relative depth estimation from a single image problem, as a ranking problem. By reformulating the problem this way, we were able to utilize literatu…
EfficientSeg: An Efficient Semantic Segmentation Network
Vahit Bugra Yesilkaynak, Yusuf H. Sahin, Gozde Unal
Deep neural network training without pre-trained weights and few data is shown to need more training iterations. It is also known that, deeper models are more successful than their…
DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning
Gulcin Baykal, Gozde Unal
Generative Adversarial Networks (GANs) triggered an increased interest in problem of image generation due to their improved output image quality and versatility for expansion towar…