activity
20162025
most citedGLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation

18 citations · 34 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2025

Facial Attribute Based Text Guided Face Anonymization

Mustafa İzzet Muştu, Hazım Kemal Ekenel

The increasing prevalence of computer vision applications necessitates handling vast amounts of visual data, often containing personal information. While this technology offers sig…

cs.CV2025

Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos

Mustafa İzzet Muştu, Hazım Kemal Ekenel

The increasing demand for large-scale visual data, coupled with strict privacy regulations, has driven research into anonymization methods that hide personal identities without ser…

cs.CV2024

Impact of Face Alignment on Face Image Quality

Eren Onaran, Erdi Sarıtaş, Hazım Kemal Ekenel

Face alignment is a crucial step in preparing face images for feature extraction in facial analysis tasks. For applications such as face recognition, facial expression recognition,…

cs.CV2024

Analyzing the Feature Extractor Networks for Face Image Synthesis

Erdi Sarıtaş, Hazım Kemal Ekenel

Advancements like Generative Adversarial Networks have attracted the attention of researchers toward face image synthesis to generate ever more realistic images. Thereby, the need…

cs.CV2024

Analyzing the Effect of Combined Degradations on Face Recognition

Erdi Sarıtaş, Hazım Kemal Ekenel

A face recognition model is typically trained on large datasets of images that may be collected from controlled environments. This results in performance discrepancies when applied…

cs.CV202418 cited

GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation

Ziya Ata Yazıcı, İlkay Öksüz, Hazım Kemal Ekenel

Convolutional Neural Networks (CNNs) have become widely adopted for medical image segmentation tasks, demonstrating promising performance. However, the inherent inductive biases in…