SS-13:Explainable Machine Learning for Image Processing
SS-08:Dynamic Background Reconstruction/Subtraction for Challenging Environments
ARS-14: Machine Learning for Image and Video Classification IV
SS-08:Dynamic Background Reconstruction/Subtraction for Challenging Environments
In this paper, we examine the overfitting behavior of image classification models modified with Implicit Background Estimation (SCrIBE), which transforms them into weakly supervised segmentation models that provide spatial domain visualizations …
Semi-supervised learning provides a means to leverage unlabeled data when labels are expensive to obtain. In this work, we propose a constrained framework that better learns from unlabeled data. The proposed algorithm adds an auxiliary task, image …
Scene understanding and semantic segmentation are at the core of many computer vision tasks, many of which, involve interacting with humans in potentially dangerous ways. It is therefore paramount that techniques for principled design of robust …