This project is focused on effectively and robustly exploiting low-quality (LQ) visual data for computer vision tasks. While most current computer vision systems are designed for high-quality visual data, collected from "clear" environments where subjects are well observable without significant attenuation or alteration, a dependable vision system must reckon with the entire spectrum of degradations from unconstrained environments. With various degradations arising from the visual data acquisition and processing pipeline, the ubiquitous LQ visual data can dramatically deteriorate the model performance in practice. The project outcome can broadly benefit a variety of real-world applications, such as video surveillance, autonomous/assisted driving, robotics and medical image analysis, where LQ visual data has constituted major performance and reliability bottlenecks.
This research categorizes common degradations into the two types: "passive degradations" that are caused by uncontrollable environment factors (such as bad weather and low light); and "active degradations" that are intentionally introduced in a controllable way to meet certain budget requirements (such as lossy compression). The project will mainly addresses two important technical questions: i) how to overcome passive degradations and achieve more robust high-level task performance on LQ video data, using end-to-end deep learning models; and ii) how to properly introduce and control active degradations to generate the desired form of LQ data, that both satisfies certain budget requirements and maintains the target task utility, using deep adversarial learning models. The resulting new techniques are to be verified on application examples such as video recognition, video annotation, video compression, and de-identified video data sharing for recognition purpose.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
