Ayşegül Dündar
Asst. Prof. at Bilkent University
Ankara, Turkey

Google Scholar  
I am an Assistant Professor of Computer Science at Bilkent University, Ankara, Turkey. Previously, I was a senior research scientist at NVIDIA, Santa Clara, USA. I received my Ph.D. degree at Purdue University, under the supervision of Professor Eugenio Culurciello, in 2016. My research was focused on embedded vision systems and was featured in popular technology journals such as MIT Technology Review and BBC. I received a B.Sc. degree in Electrical and Electronics Engineering from Bogazici University in Turkey, in 2011. My current research focuses on deep learning algorithms for computer vision.
I lead the Generative Deep Learning Research Lab at Bilkent University. Please visit the web-page.. Prospective students, please see this page.  

Recent Publications:

   Warping the Residuals for Image Editing with StyleGAN
Ahmet Burak Yildirim, Hamza Pehlivan, Aysegul Dundar
in submission
Paper  
   Diverse Semantic Image Editing with Style Codes
Hakan Sivuk, Aysegul Dundar
in submission
Paper Web-page Code 
   Inst-Inpaint: Instructing to Remove Objects with Diffusion Models
Ahmet Burak Yildirim, Vedat Baday, Erkut Erdem, Aykut Erdem, Aysegul Dundar
in submission
Paper Web-page Code 
   Refining 3D Human Texture Estimation from a Single Image
Said Fahri Altindis, Adil Meric, Yusuf Dalva, Ugur Gudukbay, Aysegul Dundar
in submission
Paper Web-page Code 
   Learning Portrait Drawing with Unsupervised Parts
Burak Tasdemir, Mustafa Goktan Gudukbay, Dogac Eldenk, Adil Meric, Aysegul Dundar
International Journal of Computer Vision (IJCV), 2023
Paper  
   Diverse Inpainting and Editing with GAN Inversion
Ahmet Burak Yildirim, Hamza Pehlivan, Bahri Batuhan Bilecen, Aysegul Dundar
IEEE International Conference on Computer Vision (ICCV), 2023
Paper  
   Progressive Learning of 3D Reconstruction Network from 2D GAN Data
Aysegul Dundar, Jun Gao, Andrew Tao, Bryan Catanzaro
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023
Paper  
   StyleRes: Transforming the Residuals for Real Image Editing with StyleGAN
Hamza Pehlivan, Yusuf Dalva, Aysegul Dundar
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Paper Web-page Code 
   Image-to-Image Translation with Disentangled Latent Vectors for Face Editing
Yusuf Dalva, Hamza Pehlivan, Oyku Irmak Hatipoglu, Cansu Moran, Aysegul Dundar
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023
Paper Web-page Code 
   Fine Detailed Texture Learning for 3D Meshes with Generative Models
Aysegul Dundar, Jun Gao, Andrew Tao, Bryan Catanzaro
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023
Paper  
   Benchmarking the Robustness of Instance Segmentation Models
Yusuf Dalva, Hamza Pehlivan, Said Fahri Altındis, Aysegul Dundar
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2023
Paper  
   VecGAN: Image-to-Image Translation with Interpretable Latent Directions
Yusuf Dalva, Said Fahri Altındis, Aysegul Dundar
European Conference on Computer Vision (ECCV) 2022
Paper Web-page Code 
   Partial Convolution for Padding, Inpainting, and Image Synthesis
Guilin Liu*, Aysegul Dundar*, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Xiaodong Yang, Andrew Tao, Bryan Catanzaro (*equal contribution)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022
Paper  
   Using Orthogonality of Weights for Improved Quantization in Deep Neural Networks
Sukru Burc Eryilmaz, Aysegul Dundar
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2022
Paper  
   Dual Contrastive Loss and Attention for GANs
Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, and Mario Fritz
IEEE International Conference on Computer Vision (ICCV) 2021
Paper  
   View Generalization for Single Image Textured 3D Models
Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021
Paper Web-page  
   Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos
Aysegul Dundar, Kevin J. Shih, Animesh Garg, Robert Pottorf, Andrew Tao, Bryan Catanzaro
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2021
Paper Code  
   Neural FFTs for Universal Texture Image Synthesis
Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro
Neural Information Processing Systems (NeurIPS) 2020
Paper
   Panoptic-based Image Synthesis
Aysegul Dundar, Karan Sapra, Guilin Liu, Andrew Tao, Bryan Catanzaro
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020
Paper
   Domain Stylization: A Fast Covariance Matching Framework towards Domain Adaptation
Aysegul Dundar, Ming-Yu Liu, Zhiding Yu, Ting-Chun Wang, John Zedlewski, Jan Kautz
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020
Paper
   Unsupervised Video Interpolation Using Cycle Consistency
Fitsum A Reda, Deqing Sun, Aysegul Dundar, Mohammad Shoeybi, Guilin Liu, Kevin J Shih, Andrew Tao, Jan Kautz, Bryan Catanzaro
IEEE International Conference on Computer Vision (ICCV) 2019
Paper Code  

Patents:

  • Domain stylization using a neural network model. US Patent 10,984,286
  • Computing architecture with concurrent programmable data co-processor. US Patent 9,858,220
  • Video interpolation using one or more neural networks. US Patent App. 16/559,312
  • Video prediction using one or more neural networks. US Patent App. 16/558,620
  • Fourier Transform-based Image Synthesis Using Neural Networks. US Patent App. 17/039,805
  • Disentanglement of Image Attributes Using a Neural Network. US Patent App. 17/678,666
  • Awards and Funding:

  • Received TÜBİTAK 3501 support, 2021
  • Awarded the Marie Skłodowska-Curie Individual Fellowship by the European Research Commission, 2020
  • 1st place, Domain Adaptation for Semantic Segmentation Competition, WAD Challenge, CVPR 2018