On Color and Symmetries for Data Efficient Deep Learning

Computer vision algorithms are getting more advanced by the day and slowly approach human-like capabilities, such as detecting objects in cluttered scenes and recognizing facial expressions. Yet, computers learn to perform these tasks very differently from humans. Where humans can generalize between different lighting conditions or geometric...

Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach

This work investigates how prior knowledge from physics-based reflection models can be used to improve the performance of semantic segmentation models under an illumination-based domain shift. We implement various color invariants as a preprocessing step and find that CNNs trained on these color invariants get stuck in worse local minima...