26 citations · 35 across the 9 of their papers we have counts for
9 papers · 1 filter
ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements
M. Arda Aydın, Efe Mert Çırpar, Elvin Abdinli +2
Recent advances in foundational Vision Language Models (VLMs) have reshaped the evaluation paradigm in computer vision tasks. These foundational models, especially CLIP, have accel…
PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification
Mert Gulsen, Batuhan Cengiz, Yusuf H. Sahin +1
Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud…
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition
Batuhan Cengiz, Mert Gulsen, Yusuf H. Sahin +1
Point clouds and meshes are widely used 3D data structures for many computer vision applications. While the meshes represent the surfaces of an object, point cloud represents sampl…
Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns
Sevgi Altun, Mustafa Cem Gunes, Yusuf H. Sahin +3
This study integrates artificial intelligence and computational design tools to extract information from architectural heritage. Photogrammetry-based point cloud models of brick wa…
ODFNet: Using orientation distribution functions to characterize 3D point clouds
Yusuf H. Sahin, Alican Mertan, Gozde Unal
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neu…
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation
Alican Mertan, Yusuf Huseyin Sahin, Damien Jade Duff +1
We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation…
EfficientSeg: An Efficient Semantic Segmentation Network
Vahit Bugra Yesilkaynak, Yusuf H. Sahin, Gozde Unal
Deep neural network training without pre-trained weights and few data is shown to need more training iterations. It is also known that, deeper models are more successful than their…
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation
Mehmet Aygün, Yusuf Hüseyin Şahin, Gözde Ünal
In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in t…
Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
Sahin Olut, Yusuf Huseyin Sahin, Ugur Demir +1
Magnetic Resonance Angiography (MRA) has become an essential MR contrast for imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions are typically ordered…