5 accepted papers
Visual-inertial odometry (VIO) is fundamental to autonomous driving, embodied navigation, and robotics. While geometry based methods dominate the field, learning based approaches are still in their infancy despite promising progress. Current deep learning based VIO methods usually fuse visual images
Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source settings. In this paper, we consider a third lesser-known setting where a training domain is endowed with a collection of pa…
Adversarial examples are intentionally designed images to force convolution neural networks to give error classification outputs. Existing attacks have constructed transferable adversarial examples from the base attack algorithm, data augmentation, ensemble model, etc. Nevertheless, under the black-…
The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate explaining the whole output, which is a probability distribution over multiple classes. A whole-output explanation can help…
In this paper, we propose a novel robust downlink beamforming (BF) design for satellite-terrestrial integrated networks. Under a realistic assumption that the angular information of eavesdroppers is not perfectly known, we establish an optimization framework for hybrid BF at the terrestrial base sta…