The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
Computer Vision has a long history of academic research, recent advances in deep learning have provided significant improvements in the ability to understand visual content. As a result of these research advances on problems such as object classification, object detection, and image segmentation, there has been a rapid increase in the adoption of Computer Vision in industry; however, mainstream Computer Vision research has given little consideration to speed or computation time, and even less to constraints such as power/energy, memory footprint and model size.
Co-organised by JOANNEUM RESEARCH - DIGITAL, this workshop has three main goals on solving and discussing efficiency in Computer Vision.
- First, the workshop aims to create a venue for a consideration of the new generation of problems that arise as Computer Vision meets mobile and AR/VR systems constraints, to bring together researchers, educators and practitioners who are interested in techniques as well as applications of compact, efficient neural network representations.
- Second, the workshop aims at reproducibility and comparability of methods for compact and efficient neural network representations, and on-device machine learning.
- Third, the workshop aims to discuss the next steps in developing efficient feature representations from three aspects: energy efficient, label efficient, and sample efficient.
The workshop’s topics are strongly related to the activities on compressing neural networks for the description and analysis of multimedia content in MPEG (Moving Picture Experts Group), which is co-chaired by Werner Bailer from DIGITAL.