Özgür Kara
Health Care Engineering Systems Center
1206 W Clark St. UIUC
Urbana, IL, USA, 61801
I am a PhD student at UIUC CS PhD program under the supervision of Founder Professor James Rehg.
My ultimate research objective is to develop controllable and computationally efficient generative models for video applications including but not limited to text-to-video generation, video editing, long-term video generation. Beyond these, I also worked on continual learning, and inverse image problems during my previous internships.
I always look for self-motivated students who want to focus on Generative AI related projects. Feel free to reach out to me if you are interested and located at UIUC.
Download my CV.
Education
- PhD (Transferred): Computer Science - UIUC - 2024-Present
- MSc, PhD: Machine Learning - Georgia Institute of Technology - 2022-2024
- BSc: Electrical-Electronics Engineering - Bogazici University - 2018-2022
- High School: Math and Science - Kadikoy Anadolu High School - 2013-2018
news
| Dec-2024 | The 6th edition of our workshop, CVEU (AI for Creative Visual Content Generation, Editing, and Understanding), where I serve as the primary organizer, has been accepted for CVPR 2025. Stay tuned! |
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| Sep-2024 | 2 papers have been submitted to CVPR’25. Stay tuned! |
| Sep-2024 | I have been recognized as an Outstanding Reviewer for ECCV 2024! |
| Jun-2024 | I am joining to CVPR’24 at Seattle with the highlight paper! Don’t forget to drop by our poster RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models which will take place on Wednesday, 19th, from 17:15 to 18:45 during Poster Session 2 in Exhibit Hall (Arch 4A-E). |
| May-2024 | I’ve started my internship at Adobe at San Jose, California, and working on text-to-video generation! |
selected publications
- In Submission
ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion ModelsIn Submission, 2024ShotAdapter enables text-to-multi-shot video generation with minimal fine-tuning, providing users control over shot number, duration, and content through shot-specific text prompts, along with a multi-shot video dataset collection pipeline. - In Submission
Optimization-Free Image Immunization Against Diffusion-Based EditingIn Submission, 2024DiffVax is an optimization-free image immunization framework that effectively protects against diffusion-based editing, generalizes to unseen content, is robust against counter-attacks, and shows promise in safeguarding video content. - In Submission to TPAMI
Towards Social AI: A Survey on Understanding Social InteractionsIn Submission to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024This is the first survey to provide a comprehensive overview of machine learning studies on social understanding, encompassing both verbal and non-verbal approaches. - ECCVW 2024 (Oral)
- CVPR 2024 (Highlight)
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion ModelsCVPR (Highlight), 2024RAVE is a zero-shot, lightweight, and fast framework for text-guided video editing, supporting videos of any length utilizing text-to-image pretrained diffusion models. - IEEE FG 2024
Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced DatasetsIEEE International Conference on Automatic Face and Gesture Recognition (IEEE FG), 2024This study provides a publicly available cross-dataset transfer learning benchmark from two existing public Turkish SLR datasets. - CVPR 2022
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCVPR, 2022ISNAS-DIP is an image-specific Neural Architecture Search (NAS) strategy designed for the Deep Image Prior (DIP) framework, offering significantly reduced training requirements compared to conventional NAS methods. - IEEE TAC
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit RecognitionIEEE Transactions on Affective Computing, 2022we propose the novel use of Continual Learning (CL), in particular, using Domain-Incremental Learning (Domain-IL) settings, as a potent bias mitigation method to enhance the fairness of Facial Expression Recognition (FER) systems. - LEAP-HRI 2021
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit RecognitionWorkshop on Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI), 16th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2021We propose the novel use of Continual Learning (CL) as a potent bias mitigation method to enhance the fairness of Facial Expression Recognition (FER) systems. - Nano Communication Networks
Molecular index modulation using convolutional neural networksNano Communication Networks, 2022We propose a novel convolutional neural network-based architecture for a uniquely designed molecular multiple-input-single-output topology, aimed at mitigating the detrimental effects of molecular interference in nano molecular communication. - Brain Stimulation
Neuroweaver: a platform for designing intelligent closed-loop neuromodulation systemsBrain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 2021Our interactive platform enables the design of neuromodulation pipelines through a visually intuitive and user-friendly interface. (Google Summer of Code 2021 project)
