3 accepted papers
Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled understanding of how supervised time-series representations can be leveraged for reliable detection under distributional shifts. This work formulate
Object detection forms a key component in Unmanned Aerial Vehicles (UAVs) for completing high-level tasks that depend on the awareness of objects on the ground from an aerial perspective. In that scenario, adversarial patch attacks on an onboard object detector can severely impair the performance of…
Object detection techniques for autonomous Un-manned Aerial Vehicles (UAV) are built upon Deep Neural Networks (DNN), which are known to be vulnerable to adversarial patch perturbation attacks that lead to object detection evasion. Yet, current adversarial patch generation schemes are not designed f…