4 papers
Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models
Stanislav Panev, Minhyek Jeon, Vaishnavi Khindkar +5
Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes. Beyond data augmentation, their potential as diagnostic t…
In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing
Xiao Fang, Yiming Gong, Stanislav Panev +4
Deep neural networks (DNNs) have achieved remarkable success in computer vision but remain highly vulnerable to adversarial attacks. Among them, camouflage attacks manipulate an ob…
Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection
Mikael Yeghiazaryan, Sai Abhishek Siddhartha Namburu, Emily Kim +4
Detecting vehicles in aerial images is difficult due to complex backgrounds, small object sizes, shadows, and occlusions. Although recent deep learning advancements have improved o…
Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision
Xiao Fang, Minhyek Jeon, Zheyang Qin +5
Detecting vehicles in aerial imagery is a critical task with applications in traffic monitoring, urban planning, and defense intelligence. Deep learning methods have provided state…