CV

For more formal version, please refer to here.

Objective

• Be kind.
• Make my parents proud.
• Conduct research on images.
• Become a successful computer scientist.
• Share my knowledge with anyone I can reach.

Education

• Ph.D. in Computer Science, Özyeğin University, 2019 - 2025
• M.Sc. in Computer Science, Özyeğin University, 2018 - 2019
• B.Sc. in Computer Science in Engineering, Özyeğin University, 2012 - 2018

Experience

• Co-Founder & Lead AI Engineer, Sep 2019 - Present
  • T-Fashion @ Istanbul, Turkey & Toronto, Canada
  • Fashion Trend Forecasting & Generative AI Design
• Research Assistant, Feb 2019 - Present
  • Özyeğin University @ Istanbul, Turkey
  • Computer Science Department
• Computer Vision Research Engineer, Jun 2020 - Dec 2024
  • Fishency Innovation @ Stavanger, Norway
  • Contract-based
• Machine Learning Engineer, Aug 2018 - Jan 2019
  • eBay Turkey, GittiGidiyor @ Istanbul, Turkey
  • R&D Team
• Software Engineer, Jun 2018 - Aug 2018
  • Türk Telekom A.Ş. @ Istanbul, Turkey
  • Business Intelligence & Data Team

Skills

• Python - C++ - Java
• PyTorch - Keras
• OpenCV - Kornia
• Cloud Services (GC, AWS, IBM Cloud)

Publications

Clothing Image Retrieval with Triplet Capsule Networks / A Thesis

F. Kinli. Clothing Image Retrieval with Triplet Capsule Networks. (Master's thesis, Özyeğin University, Istanbul, Turkey), 2019.

Fashion Image Retrieval with Capsule Networks

F. Kinli, B. Ozcan and F. Kirac. Fashion Image Retrieval with Capsule Networks. In Proceedings of The IEEE International Conference on Computer Vision (ICCV) Workshops, Oct, 2019.

FashionCapsNet: Clothing Classification with Capsule Networks

KINLI, F , KIRAÇ, F . (2020). FashionCapsNet: Clothing Classification with Capsule Networks. Bilişim Teknolojileri Dergisi , 13 (1) , 87-96 . DOI: 10.17671/gazibtd.580222

Description-aware Fashion Image Inpainting with Convolutional Neural Networks in Coarse-to-Fine Manner

F. Kınlı, B. Özcan and F.Kıraç. Description-aware Fashion Image Inpainting with Convolutional Neural Networks in Coarse-to-Fine Manner. In Proceedings of 2020 6th International Conference on Computer and Technology Applications (ICCTA 2020, former ICCIT), Apr, 2020.

A Benchmark for Inpainting of Clothing Images with Irregular Holes

Kınlı, F., Özcan, B., & Kıraç, F. (2020). A Benchmark for Inpainting of Clothing Images with Irregular Holes. arXiv preprint arXiv:2007.05080.

Quaternion Capsule Networks

Özcan, B., Kınlı, F. & Kıraç, F. (2020). Quaternion Capsule Networks. arXiv preprint arXiv:2007.04389.

Re: Spatial-Adaptive Network for Single Image Denoising

Mentes, S., Kınlı, F., Özcan, B. & Kıraç, F. (2021). [Re] Spatial-Adaptive Network for Single Image Denoising. ML Reproducibility Challenge 2020. https://openreview.net/forum?id=yiAI9QN9nYt

Re: VCNet: A Robust Approach to Blind Image Inpainting

Kınlı, F., Özcan, B. & Kıraç, F. (2021). Re: VCNet: A Robust Approach to Blind Image Inpainting. ML Reproducibility Challenge 2020. https://openreview.net/pdf?id=RKAKTnLzb8q

Instagram Filter Removal on Fashionable Images

Furkan Kinli, Baris Ozcan, Furkan Kirac; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 736-745

[Re] Lifting 2D StyleGAN for 3D-Aware Face Generation

Doğa Yılmaz, Furkan Kınlı, Barış Özcan and Furkan Kıraç. (2022). [Re] Lifting 2D StyleGAN for 3D-Aware Face Generation. ML Reproducibility Challenge 2021 (Fall Edition). https://openreview.net/forum?id=BcNonfQ3RY

Patch-wise Contrastive Style Learning for Instagram Filter Removal

Kınlı, F., Özcan, B., & Kıraç, F. (2022). Patch-wise Contrastive Style Learning for Instagram Filter Removal. arXiv preprint arXiv:2204.07486.

Generalization to Unseen Viewpoint Images of Objects via Alleviated Pose Attentive Capsule Agreement

Özcan, B., Kınlı, F. & Kıraç, F. Generalization to unseen viewpoint images of objects via alleviated pose attentive capsule agreement. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-022-07900-3

Modeling the Lighting in Scenes as Style for Auto White-Balance Correction

Kınlı, F., Yılmaz, D., Özcan, B., and Kıraç, F. (2022). Modeling the Lighting in Scenes as Style for Auto White-Balance Correction. arXiv preprint arXiv:22210.09090.

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Kınlı, F., et al. (2022). AIM 2022 Challenge on Instagram Filter Removal: Methods and Results. arXiv preprint arXiv:2210.08997.

Reversing Image Signal Processors by Reverse Style Transferring

Kınlı, F., Özcan, B., & Kıraç, F. (2022). Reversing Image Signal Processors by Reverse Style Transferring. arXiv preprint arXiv:2210.09074.

Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

Conde, M. V., et al. (2022). Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report. arXiv preprint arXiv:2210.11153.

Academic Services

• Reviewer, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• Reviewer, Elsevier Digital Signal Processing
• Reviewer, IEEE/CAA Journal of Automatica Sinica
• Reviewer, Imaging Science Journal
• Reviewer, Journal of Experimental & Theoretical Artificial Intelligence
• Reviewer, the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) (2025, 2024, 2023)
• Reviewer, the International Conference on Computer Vision (ICCV) (2025, 2023)
• Reviewer, the European Conference on Computer Vision (ECCV) (2024, 2022)
• Reviewer, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2025, 2024, 2023)
• Reviewer, The 39th Annual AAAI Conference on Artificial Intelligence (AAAI) 2025
• Reviewer, International Conference on Pattern Recognition (ICPR) 2024
• Reviewer, New Trends in Image Restoration and Enhancement workshop and challenges on image and video processing at CVPR (NTIRE) (2024, 2023, 2022, 2021)
• Reviewer, Advances in Image Manipulation workshop in conjunction with ECCV (AIM) (2024, 2022, 2020)
• Reviewer, Resource Efficient Deep Learning for Computer Vision workshop at ICCV (RCV) (2023)
• Reviewer, 30th IEEE Conference on Signal Processing and Communications Applications
• Reviewer, ML Reproducibility Challenge 2022 Edition
• Reviewer, ML Reproducibility Challenge 2021 Edition