Mert Bulent Sariyildiz

Research Scientist at NAVER LABS Europe

I was a doctoral researcher at NAVER LABS Europe and Inria Grenoble (the THOTH Team) in France, under the supervision of Yannis Kalantidis, Diane Larlus and Karteek Alahari. My PhD focused on learning general-purpose visual representations from images.

I received my M.Sc. degree from the Computer Engineering Department at Bilkent University in Türkiye, where I worked with Gokberk Cinbis on learning data-efficient visual classification models. Before that, I received my B.Sc. from the Electrical and Electronics Engineering Department at Anadolu (now Eskisehir Technical) University in Türkiye.

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

profile photo


I'm broadly interested in computer vision problems. If I have to be more specific, I like working on learning visual representations from imagery data with different forms of supervision (including no supervision at all!) so that they are useful for a range of vision tasks.

Ret4Loc synthetic variants Ret4Loc | Tailoring Retrieval Representations to Long-term Visual Localization
Yannis Kalantidis*, Mert Bulent Sariyildiz*, Rafael S. Rezende, Philippe Weinzaepfel, Diane Larlus and Gabriela Csurka
ICLR 2024

Visual localization methods generally rely on a first image retrieval step whose role is crucial. In this paper, we improve this retrieval step and tailor it to the final localization task. We propose to synthesize variants of the training set images, obtained from generative text-to-image models, in order to automatically expand the training set towards a number of nameable variations that particularly hurt visual localization.

ImageNet-SD model overview ImageNet-SD | Fake it till you make it: Learning(s) from a synthetic ImageNet clone
Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus and Yannis Kalantidis
CVPR 2023

Recent text-to-image generative models, generate fairly realistic images. Could such models render real images obsolete for training image prediction models? We answer part of this provocative question by questioning the need for real images when training models for ImageNet-1K classification. We show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data.

ImageNet-1K vs. transfer performance plane t-ReX | No reason for no supervision: Improving the generalization of supervised models
Mert Bulent Sariyildiz, Yannis Kalantidis, Karteek Alahari and Diane Larlus
ICLR 2023

We revisit supervised learning on ImageNet-1K and propose a training setup which improves transfer learning performance of supervised models.
project website, OpenReview

Hypothetical ImageNet-CoG levels ImageNet-CoG | Concept Generalization in Visual Representation Learning
Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus and Karteek Alahari
ICCV 2021

We propose a benchmark tailored for measuring concept generalization capabilities of models trained on ImageNet-1K.
project website, code, poster, presentation (PDF), presentation (PPT), video

Mixing negatives in MoCHi MoCHi | Hard Negative Mixing for Contrastive Learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel and Diane Larlus
NeurIPS 2020

For contrastive learning, sampling more or harder negatives often improve performance. We propose two ways to synthesize more negatives using the MoCo framework.
project website

Masked token attention in ICMLM ICMLM | Learning Visual Representations with Caption Annotations
Mert Bulent Sariyildiz, Julien Perez and Diane Larlus
ECCV 2020

Images often come with accompanying text describing the scene in images. We propose a method to learn visual representations using (image, caption) pairs.
project website, demo

keyp-compute-chain Key protected classification for collaborative learning
Mert Bulent Sariyildiz, Ramazan Gokberk Cinbis and Erman Ayday
Pattern Recognition, Vol. 104, August 2020

Vanilla collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network attack. We propose a classification model that is resilient against such attacks by design.
code repo

gmn-front-figure GMN | Gradient Matching Generative Networks for Zero-Shot Learning
Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis
CVPR 2019, oral presentation

Zero-shot learning models may suffer from the domain-shift due to the difference between data distributions of seen and unseen concepts. We propose a generative model to synthesize samples for unseen concepts given their visual attributes and use these samples for training a classifier for both seen and unseen concepts.
code repo

Community Service

Huge thanks to Jon Barron, who provides the template of this website.