Vision is a rich and un-intrusive sensor modality that can be captured by cheap and flexible sensors. However, its strengths are also its downside as it is challenging to extract what is relevant from the high dimensional signal. Recently computer vision has experienced what can only be referred to as a revolution. The second coming of neural networks, known as deep learning, has lead to a significant increase in performance in tasks across the field. It is now possible to learn image representations directly from data rather than relying on ad-hoc handcrafted features.
The aim of this workshop is to bring together researchers to discuss both theoretical and practical issues related to the application of deep vision techniques for intelligent vehicles. We want to demonstrate what computer vision currently is capable of and identify important directions of future work. The workshop will be centered around a set of invited talks from prominent researchers together with a poster session of submitted extended abstracts as well as a tutorial in the afternoon. The tutorial will focus on the software library caffe, one of the most popular convolutional neural network toolboxes for vision tasks.
We invite the submissions of extended abstracts for poster contributions to the workshop. Topics of interest are:
- Deep learning for reinforcement learning
- Detection and segmentation networks
- Uncertainty propagation in deep neural networks
- Efficient inference with deep neural networks
- Deep neural networks for perception systems in intelligent vehicles
- Datasets for autonomous driving