3 accepted papers
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 semi-supervised student-teacher framework. However a fundamental issue arises from the class imbalance in the labell…
LIDAR-based 3D object detection aims to enhance the situational awareness of autonomous vehicles. Despite recent advancements in this technology, there has been evidence that the susceptibility of 3D object detectors to signal spoofing is high, leading to the erroneous detection of "ghost objects" o…
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. False-positive error propagation is still observed in methods using the well-established student-teacher fra…