Despite the success of CNNs, selecting the optimal architecture for a given task remains an
open problem. Instead of aiming to select a single optimal architecture, we propose …

Road detection and centerline extraction from very high-resolution (VHR) remote sensing
imagery are of great significance in various practical applications. Road detection and …

Circuit failure prediction predicts the occurrence of a circuit failure before errors actually
appear in system data and states. This is in contrast to classical error detection where a …

Deep neural networks have advanced many computer vision tasks, because of their
compelling capacities to learn from large amount of labeled data. However, their …

Face segmentation is the task of densely labeling pixels on the face according to their
semantics. While current methods place an emphasis on develo** sophisticated …

Recent years have seen a sharp increase in the number of related yet distinct advances in
semantic segmentation. Here, we tackle this problem by leveraging the respective strengths …

In recent years, computer vision research has witnessed transformative changes with the
integration of generative artificial intelligence (AI) models. The generative models have been …

The paper adopts two different convolution sampling paths: from large scale to small scale
sampling (top-down) and small scale to large scale sampling (bottom-up), and propose the …

In deep convolutional networks for segmentation, resolution is significantly reduced by
multiple pooling and convolution operations, which makes the prediction accuracy of pixel …

In this dissertation, we propose methods and data driven machine learning solutions which
address and benefit from the recent overwhelming growth of digital media content. First, we …