An increasing number of artificial intelligence (AI) applications involve the execution of deep neural networks (DNNs) on edge devices. Many practical reasons motivate the need to update the DNN model on the edge device post-deployment, such as …
Data collected by Earth-observing (EO) satellites are often afflicted by cloud cover. Detecting the presence of clouds -- which is increasingly done using deep learning -- is crucial preprocessing in EO applications. In fact, advanced EO satellites …
We propose novel techniques to make object pose estimation remarkably robust against occlusions.
Deep neural networks (DNNs) have become essential for processing the vast amounts of aerial imagery collected using earth-observing satellite platforms. However, DNNs are vulnerable towards adversarial examples, and it is expected that this weakness …
We propose BPnP, a network module for backpropagation through a PnP optimizer, as if the optimizer were a differentiable function.
Relative scores such as Local Outlying Factor and mass ratio have been shown to be better scores than global scores in detecting anomalies. While this is true, our analysis reveals for the first time that these relative scores have a key shortcoming: …
We proposed an approach to estimate the 6DOF pose of a satellite. Our method won the first place in the Kelvins Pose Estimation Challenge organised by the European Space Agency (ESA).
Most density-based clustering algorithms suffer from large density variations among clusters. This paper proposes a new measure called Neighbourhood Contrast (NC) as a better alternative to density in detecting clusters. The proposed NC admits all …