14 citations · 24 across the 10 of their papers we have counts for
13 papers
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren +1
Hyperspectral Imaging (HSI) plays an increasingly critical role in precise vision tasks within remote sensing, capturing a wide spectrum of visual data. Transformer architectures h…
What Can AutoML Do For Continual Learning?
Mert Kilickaya, Joaquin Vanschoren
This position paper outlines the potential of AutoML for incremental (continual) learning to encourage more research in this direction. Incremental learning involves incorporating…
Locality-Aware Hyperspectral Classification
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren
Hyperspectral image classification is gaining popularity for high-precision vision tasks in remote sensing, thanks to their ability to capture visual information available in a wid…
AdaCL:Adaptive Continual Learning
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya +1
Class-Incremental Learning aims to update a deep classifier to learn new categories while maintaining or improving its accuracy on previously observed classes. Common methods to pr…
Towards Label-Efficient Incremental Learning: A Survey
Mert Kilickaya, Joost van de Weijer, Yuki M. Asano
The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assume…
Are Labels Needed for Incremental Instance Learning?
Mert Kilickaya, Joaquin Vanschoren
In this paper, we learn to classify visual object instances, incrementally and via self-supervision (self-incremental). Our learner observes a single instance at a time, which is t…
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts
Tommie Kerssies, Mert Kılıçkaya, Joaquin Vanschoren
In this paper, our goal is to adapt a pre-trained convolutional neural network to domain shifts at test time. We do so continually with the incoming stream of test batches, without…
Human-Object Interaction Detection via Weak Supervision
Mert Kilickaya, Arnold Smeulders
The goal of this paper is Human-object Interaction (HO-I) detection. HO-I detection aims to find interacting human-objects regions and classify their interaction from an image. Res…
Structured Visual Search via Composition-aware Learning
Mert Kilickaya, Arnold W. M. Smeulders
This paper studies visual search using structured queries. The structure is in the form of a 2D composition that encodes the position and the category of the objects. The transform…
Self-Selective Context for Interaction Recognition
Mert Kilickaya, Noureldien Hussein, Efstratios Gavves +1
Human-object interaction recognition aims for identifying the relationship between a human subject and an object. Researchers incorporate global scene context into the early layers…
Diagnosing Rarity in Human-Object Interaction Detection
Mert Kilickaya, Arnold Smeulders
Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined…
Continual Learning of Object Instances
Kishan Parshotam, Mert Kilickaya
We propose continual instance learning - a method that applies the concept of continual learning to the task of distinguishing instances of the same object category. We specificall…
Re-evaluating Automatic Metrics for Image Captioning
Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis +1
The task of generating natural language descriptions from images has received a lot of attention in recent years. Consequently, it is becoming increasingly important to evaluate su…