no code implementations • 28 Mar 2024 • Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag
Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation, offering a highly effective way to adapt and refine pre-trained deep learning models for specific tasks without the need for comprehensive retraining.
1 code implementation • 26 Mar 2024 • Ibrahim Ethem Hamamci, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, Emine Bensu Erdemir, Abdullah Alanbay, Anjany Sekuboyina, Berkan Lafci, Mehmet K. Ozdemir, Bjoern Menze
A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets.
no code implementations • 14 Dec 2023 • Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari
Diffusion models (DMs) have gained prominence due to their ability to generate high-quality, varied images, with recent advancements in text-to-image generation.
no code implementations • 11 Dec 2023 • Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag
Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects.
1 code implementation • 30 May 2023 • Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Atif Emre Yuksel, Sadullah Gultekin, Serife Damla Ozdemir, Kaiyuan Yang, Hongwei Bran Li, Sarthak Pati, Bernd Stadlinger, Albert Mehl, Mustafa Gundogar, Bjoern Menze
To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023.
1 code implementation • 25 May 2023 • Ibrahim Ethem Hamamci, Sezgin Er, Anjany Sekuboyina, Enis Simsar, Alperen Tezcan, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Furkan Almas, Irem Dogan, Muhammed Furkan Dasdelen, Chinmay Prabhakar, Hadrien Reynaud, Sarthak Pati, Christian Bluethgen, Mehmet Kemal Ozdemir, Bjoern Menze
As an example, we generated 100, 000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes.
2 code implementations • 11 Mar 2023 • Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Anjany Sekuboyina, Mustafa Gundogar, Bernd Stadlinger, Albert Mehl, Bjoern Menze
To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes.
no code implementations • 2 Dec 2022 • Enis Simsar, Alessio Tonioni, Evin Pınar Örnek, Federico Tombari
3D GANs have the ability to generate latent codes for entire 3D volumes rather than only 2D images.
no code implementations • 16 Mar 2022 • Enis Simsar, Umut Kocasari, Ezgi Gülperi Er, Pinar Yanardag
We evaluate our framework with qualitative and quantitative experiments and show that our method finds more diverse and disentangled directions.
1 code implementation • 2 Dec 2021 • Enis Simsar, Evin Pınar Örnek, Fabian Manhardt, Helisa Dhamo, Nassir Navab, Federico Tombari
With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography.
1 code implementation • 22 Aug 2021 • Dilara Gokay, Enis Simsar, Efehan Atici, Alper Ahmetoglu, Atif Emre Yuksel, Pinar Yanardag
In this paper, we propose a graph-based image-to-image translation framework for generating images.
2 code implementations • ICCV 2021 • Oğuz Kaan Yüksel, Enis Simsar, Ezgi Gülperi Er, Pinar Yanardag
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs).