Çağrı Eser

I am a Computer Vision and Machine Learning researcher with over five years of industrial experience in perception in autonomous systems. I completed my MSc in Computer Engineering at METU, 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 Sinan Kalkan and Emre Akbaş.

I am interested in computer vision, explainable machine learning, and multimodal representation learning. In particular, I greatly enjoy investigating representations of multiple modalities.

Recent News

September 2026 - I’ve started a blog! Check out my first entry on building wordsim.
August 2026 - Check out wordsim, a simple word guessing game based on semantic similarities of word representations for English and Turkish word embeddings.
June 2026 - “Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition” has been accepted to the ICML2026 Workshop on Machine Learning for Audio. See you in Seoul!
- Paper link available here, with code also now available.
March 2026 - I’m serving as a reviewer for SIU2026.
February 2026 - Our latest work, “Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance”, has been published in Neurocomputing!

Older News

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 supervisors Prof. Sinan Kalkan and Prof. 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: 4.0/4.0

Selected Research

Evaluating Pretrained Music Embeddings for Cross-Performance Jazz Standard Recognition
Çağrı Eser
Accepted to ICML2026 @ Workshop on Machine Learning for Audio
We evaluate the effectiveness of music embeddings from audio pretrained models on the challenging task of standard recognition from jazz performances, and suggest a lightweight contrastive adaptation for improving retrieval-based approaches.
[PDF] | [Code]
Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance
Çağrı Eser, Zeynep Sonat Baltacı, Emre Akbaş, Sinan Kalkan
Neurocomputing 674 (2026) 132938
We propose an alternative perspective on imbalance in long-tailed datasets, focusing on the intrinsic dimensionalities of classes in image space rather than their cardinalities.
[PDF] | [Preprint] | [Code]
Mitigating class imbalance in long-tailed visual recognition
through the use of intrinsic dimensionality
Çağrı Eser
MSc thesis
I concentrate 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]