12 citations · 27 across the 6 of their papers we have counts for
6 papers · 1 filter
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation
Nikolay Jetchev, Gökhan Yildirim, Christian Bracher +1
Attention is a general reasoning mechanism than can flexibly deal with image information, but its memory requirements had made it so far impractical for high resolution image gener…
Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliency
Gökhan Yildirim, Debashis Sen, Mohan Kankanhalli +1
Salient object detection is evaluated using binary ground truth with the labels being salient object class and background. In this paper, we corroborate based on three subjective e…
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages
Nikolay Jetchev, Urs Bergmann, Gökhan Yildirim
Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material. Formally, this process takes a…
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Gökhan Yildirim, Nikolay Jetchev, Roland Vollgraf +1
Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determi…
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Nikolay Jetchev, Urs Bergmann, Gokhan Yildirim
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-int…
Disentangling Multiple Conditional Inputs in GANs
Gökhan Yildirim, Calvin Seward, Urs Bergmann
In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in…