Mikhail Kennerley

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue....

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection

Mind the Gap: Transferring Labels to Align Object Detection Datasets

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets

Combining multiple object detection datasets offers a path to improved generalisation but is hindered by inconsistencies in class semantics and bounding box annotations. Some methods to address...

CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection

Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the...