Çağrı Eser

I am a Computer Vision and Machine Learning Engineer with over five years of experience in perception systems for autonomous vehicles. I completed my MSc in Computer Engineering at METU in 2024, where I also earned my BSc and a Minor in Mathematics. My thesis addressed class imbalance in long-tailed visual recognition, and my advisors were Prof. Dr. Sinan Kalkan and Assoc. Prof. Dr. Emre Akbaş.

I am interested in a variety of topics across computer vision, deep learning, and autonomous vehicles. My current research explores the behavior of deep models under various forms of imbalance. My goal is to perform research that bridges ML theory and practice while increasing the robustness and explainability of models across multiple modalities in deep learning.

News

New! Our latest work, “Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance”, is now available on arXiv.
I’m serving as an adjunct lecturer for CENG240 for the Spring 2024 semester, a course for Programming with Python for Engineers.
- Weekly course materials can be found here in the form of Jupyter notebooks.
I have completed my Master of Sciences degree of Computer Engineering at METU! I’d like to give special thanks to my professors Sinan Kalkan and Emre Akbaş for their continual guidance throughout my studies.
- My thesis was about mitigating the effects of class imbalance in long-tailed visual recognition using intrinsic dimensionality. It can be accessed on OpenMETU through this link.
- cGPA: 4.0/4

Publications and Preprints

Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance
Çağrı Eser, Zeynep Sonat Baltacı, Emre Akbaş, Sinan Kalkan
arXiv preprint
We propose an alternative perspective on imbalance in long-tailed datasets, focusing on intrinsic properties of classes in image space rather than their cardinalities.
[arXiv] | [Code]
Mitigating class imbalance in long-tailed visual recognition
through the use of intrinsic dimensionality
Çağrı Eser
MSc thesis
I focus on model-based and data-based definitions of intrinsic dimensionality and their relation to model performance, choice of architecture, choice of estimator and imbalance severity.
[PDF]