ARS-14: Machine Learning for Image and Video Classification IV
Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential. Implicit Background Estimation (IBE) has demonstrated to be a promising technique to improve the …
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 …
Project for ECE8823 Convex Optimization course at Georgia Tech