Incorporating Human Domain Knowledge in 3-D LiDAR-Based Semantic Segmentation

Incorporating Human Domain Knowledge into Large Scale Cost Function Learning

Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human pr...

A Learning-Based Framework Incorporating Domain Knowledge for Performance Modeling

Energy efficiency has become the critical factor of computing performance in platforms from embedded devices, portable electronics to servers in datacenters. Power density and peak power demands increase in each generation of microprocessors, which directly lead to a higher operating temperature that exceeds the cooling capability available on current multi-core systems. These physical constrai...

Fully Automated Knowledge-Based Segmentation of the Caudate Nuclei in 3-D MRI

In this paper we present a fully automated approach to segmentation of the caudate nuclei in 3-D magnetic resonance images. It is based on the technique of probabilistic boosting trees. As a strategy for supervised learning it is capable to derive a discriminative model for the distinction of object and non-object voxels from expert annotated imaging data and rather rough anatomical prior knowl...

Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of ob...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models