No abstract

We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset.

In this manuscript, we design, describe, and present a functional model of Time-of-Flight (ToF) cameras. The model can be used to generate randomized scenes that incorporate depth scenarios with various objects at various depths with varied orientations and illumination intensity. In addition to the potential to generate any random depth scenario, the camera, pixels, and binning are modelled incorporating radial distortion based on camera intrinsic and extrinsic. The model also includes ToF artifacts such as Signal Noise, Crosstalk and Multipath. We measured experimentally the Noise in Time-of-Flight. We experimentally fitted, and simulated with state-of-the art Simulator the Crosstalk effect, and characterized multipath according with the existing literature. Our work can be used to generate as many images as needed for neural network (NN) training and testing. The proposed approach can also be used to benchmark and evaluate both End-to-End ToF algorithms as well as specialized algorithms for denoising, unwrapping, crosstalk, and multipath correction.