566 citations · 839 across the 37 of their papers we have counts for
12 papers · 1 filter
Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies
Ufuk Efe, Alptekin Temizel
Detectors of logical anomalies, such as missing, extra or wrongly combined parts, usually score a test image by its distance from normal (good) images. They achieve near-perfect im…
Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models
Ümit Mert Çağlar, Alptekin Temizel
Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early diagnosis is crucial for the tr…
Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation
Ümit Mert Çağlar, Alptekin Temizel
Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation…
BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe
Ümit Mert Çağlar, Alptekin Temizel
Earth observation imagery plays a critical role in environmental monitoring, urban planning, disaster assessment, and climate analysis. While multi-spectral sensors are increasingl…
LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
Ümit Mert Çağlar, Alptekin Temizel
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes…
SenBen: Sensitive Scene Graphs for Explainable Content Moderation
Fatih Cagatay Akyon, Alptekin Temizel
Content moderation systems classify images as safe or unsafe but lack spatial grounding and interpretability: they cannot explain what sensitive behavior was detected, who is invol…
Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis
Umut Dundar, Alptekin Temizel
Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pathological textures and struc…
Grounding Synthetic Data Generation With Vision and Language Models
Ümit Mert Çağlar, Alptekin Temizel
Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However, existing evaluation metrics fo…
TopoMaskV3: 3D Mask Head with Dense Offset and Height Predictions for Road Topology Understanding
Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Mask-based paradigms for road topology understanding, such as TopoMaskV2, offer a complementary alternative to query-based methods by generating centerlines via a dense rasterized…
DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI
Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of re…
D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity
Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due to complex time-varying disrup…
Local-Global Prompt Learning via Sparse Optimal Transport
Deniz Kizaroğlu, Ülku Tuncer Küçüktas, Emre Çakmakyurdu +1
Few-shot adaptation of vision-language models (VLMs) like CLIP typically relies on learning textual prompts matched to global image embeddings. Recent works extend this paradigm by…