UCLA

Deep learning has exhibited remarkable performance on various computer vision tasks. However, these models usually suffer from the generalization issue when the training sets are not sufficiently large or diverse. Human intelligence, on the other hand, is capable of learning with a few samples. One of the potential reasons for this is that we use other prior knowledge to generalize to new environments and unseen data, as opposed to learning everything from the provided training sets. We aim to enable machines with such capability. More specifically, we focus on integrating different types of prior physical knowledge and inductive biases into neural networks for various computer vision applications.

The core idea is to exploit physical models as inductive biases and design specific strategies to blend them with the neural network learning process. This problem is difficult since we need to consider both the fidelity of our prior knowledge and the quality of the training samples. To validate the effectiveness of the proposed blending strategies, extensive experiments have been conducted on multiple computer vision tasks, such as Shape from Polarization (SfP), remote photoplethysmography (rPPG), and single-image rain removal.