Cameras apply a lot of processing on the raw image recorded by the sensor to enhance the brightness, contrast, and colors, and make the output image visually pleasing. The image is also finally compressed to make the file size smaller. These operations make it difficult to reverse to the raw sensor image which is necessary […]

Humans possess the ability to see objects as having the same color even when viewed under different illuminations. Cameras inherently lack this capability. A process called auto white balance (AWB) has to be applied by the camera to mimic this behavior of the human visual system. AWB is one of the first steps in a […]

The main focus of this research is to develop representation learning architectures and algorithms that can help perform various multimodal understanding tasks, and at the same time reduce the need for human supervision in the form of costly annotations. To achieve this goal, a learning system must be able to: (1) learn new tasks or […]

Camera enabled AI-based personal assistants will need to recognize human actions in order to be safe and effective. Current machine learning approaches for action recognition require extensive datasets of annotated videos that depicting the actions to be recognized. Such datasets are expensive to acquire. The goal of this project is to decrease the annotation required […]