Ozgur Kara — accepted AI-conference papers

2026

DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing

Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali +1

ICLR 2026poster

Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming optimization for each image separately, taking hours for small batc…

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2025

DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images

Ozgur Kara, Harris Nisar, James Matthew Rehg

NeurIPS 2025poster

Numerous models have been developed for scanpath and saliency prediction, which are typically trained on scanpaths, which model eye movement as a sequence of discrete fixation points connected by saccades, while the rich information contained in the raw trajectories is often discarded. Moreover, mos…

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2025

ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models

Ozgur Kara, Krishna Kumar Singh, Feng Liu, Duygu Ceylan, James M. Rehg, Tobias Hinz

CVPR 2025poster

Current diffusion-based text-to-video methods are limited to producing short video clips of a single shot and lack the capability to generate multi-shot videos with discrete transitions where the same character performs distinct activities across the same or different backgrounds. To address this li…

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2024

RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models

Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe, James M. Rehg, Pinar Yanardag

CVPR 2024highlight

Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However video editing models have not yet reached the same level of visual quality and user control. To address this we introduce RAVE a zero-shot video editing method that leverages p…

Cited by 46 Source ↗PDF ↗code ↗Scholar ↗

2022

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

Metin Ersin Arican, Ozgur Kara, Gustav Bredell, Ender Konukoglu

CVPR 2022poster

Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in the Deep Image Prior (DIP) framework. The benefit of the inductive bias the network imposes in the DIP framework dep…

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