Search Results for author: Enis Simsar

Found 13 papers, 7 papers with code

Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models

no code implementations20 Jun 2024 Matthew Zheng, Enis Simsar, Hidir Yesiltepe, Federico Tombari, Joel Simon, Pinar Yanardag

Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation.

CLoRA: A Contrastive Approach to Compose Multiple LoRA Models

no code implementations28 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.

Image Generation

LIME: Localized Image Editing via Attention Regularization in Diffusion Models

no code implementations14 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.

Denoising Semantic Segmentation +1

CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models

no code implementations CVPR 2024 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.

DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays

1 code implementation30 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.

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

1 code implementation25 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

GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model.

Computed Tomography (CT) Image Generation +5

Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays

2 code implementations11 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.

Denoising Object +2

LatentSwap3D: Semantic Edits on 3D Image GANs

no code implementations2 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.

Feature Importance

Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs

no code implementations16 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.

Diversity Image Generation

Object-aware Monocular Depth Prediction with Instance Convolutions

1 code implementation2 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.

Depth Estimation Depth Prediction +2

LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions

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).

Contrastive Learning Image Generation

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