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20162026
most citedDartsReNet: Exploring new RNN cells in ReNet architectures

9 citations · 21 across the 12 of their papers we have counts for

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cs.CV20239 cited

DartsReNet: Exploring new RNN cells in ReNet architectures

Brian Moser, Federico Raue, Jörn Hees +1

We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS. We are interested in the ReNet architec…

cs.CV2022

DT2I: Dense Text-to-Image Generation from Region Descriptions

Stanislav Frolov, Prateek Bansal, Jörn Hees +1

Despite astonishing progress, generating realistic images of complex scenes remains a challenging problem. Recently, layout-to-image synthesis approaches have attracted much intere…

cs.CV2021

Spatial Transformer Networks for Curriculum Learning

Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio +3

Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the c…

cs.CV2021

AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style

Stanislav Frolov, Avneesh Sharma, Jörn Hees +3

Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-…

cs.CV2021

Adversarial Text-to-Image Synthesis: A Review

Stanislav Frolov, Tobias Hinz, Federico Raue +2

With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way fo…

cs.CV20207 cited

Leveraging Visual Question Answering to Improve Text-to-Image Synthesis

Stanislav Frolov, Shailza Jolly, Jörn Hees +1

Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds a…