SNIC
SUPR
SNIC SUPR
DeepVision: Deep Learning for Robot Vision
Dnr:

SNIC 2018/3-313

Type:

SNAC Medium

Principal Investigator:

Michael Felsberg

Affiliation:

Linköpings universitet

Start Date:

2018-07-01

End Date:

2019-07-01

Primary Classification:

10207: Computer Vision and Robotics (Autonomous Systems)

Allocation

Abstract

Recently, image representations based on convolutional neural networks (CNNs) have demonstrated significant improvements over the state-of-the-art in many computer vision applications including image classification, object detection, scene recognition, semantic segmentation, action recognition, and visual tracking. CNNs consist of a series of convolution and pooling operations followed by one or more fully connected (FC) layers. Deep networks are trained using raw image pixels with a fixed input size or sparse point clouds in a finite volume. These networks require large amounts of labeled training data. The introduction of large datasets (e.g. ImageNet, 14 million images, semantic 3D datasets, and synthetic datasets) and the parallelism enabled by modern GPUs have facilitated the rapid deployment of deep networks for many visual tasks. This development has led to what many peers call the deep learning revolution in computer vision. CVL is currently working on six different research tasks within the DeepVision project and that GPU-resources are requested for: 1. Visual object tracking challenge 2. Detailed semantic description of humans in images and videos 3. Deep learning for large scale remote sensing scene analysis 4. Probabilistic 3D computation from time-of-flight measurements 5. Algebraically constrained networks for sparse image data 6. Shifted exponential linear units