7 papers
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
Romain Hermary, Samet Hicsonmez, Dan Pineau +2
Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous a…
Annotation Free Spacecraft Detection and Segmentation using Vision Language Models
Samet Hicsonmez, Jose Sosa, Dan Pineau +4
Vision Language Models (VLMs) have demonstrated remarkable performance in open-world zero-shot visual recognition. However, their potential in space-related applications remains la…
Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion
Samet Hicsonmez, Abd El Rahman Shabayek, Djamila Aouada
Zero-Shot image Anomaly Detection (ZSAD) aims to detect and localise anomalies without access to any normal training samples of the target data. While recent ZSAD approaches levera…
VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion
Samet Hicsonmez, Abd El Rahman Shabayek, Djamila Aouada
Detecting visual anomalies in diverse, multi-class real-world images is a significant challenge. We introduce \ours, a novel unsupervised multi-class visual anomaly detection frame…
NSYNC: Negative Synthetic Image Generation for Contrastive Training to Improve Stylized Text-To-Image Translation
Serkan Ozturk, Samet Hicsonmez, Pinar Duygulu
Current text conditioned image generation methods output realistic looking images, but they fail to capture specific styles. Simply finetuning them on the target style datasets sti…
Domain Adaptive Object Detection for Space Applications with Real-Time Constraints
Samet Hicsonmez, Abd El Rahman Shabayek, Arunkumar Rathinam +1
Object detection is essential in space applications targeting Space Domain Awareness and also applications involving relative navigation scenarios. Current deep learning models for…