Neural Architecture Search for Computer Vision in the Wild 2020 Call for Papers

Call for Papers: 
Neural Architecture Search for Computer Vision in the Wild 2020

https://nasfw20.github.io/

The Workshop on Neural Architecture Search for Computer Vision in the
Wild will be held in conjunction with WACV 2020.

Recent years have witnessed a significant rise in research related to
neural architecture search (NAS) that allows automatically finding
deep network architectures. These architectures often achieve better
performance than the state-of-the-art methods that have been carefully
designed by deep learning researchers. Although NAS shows promise by
exhibiting superior performance on standard benchmarks such as
CIFAR-10/100 and ImageNet, the evidence is scarce that they would work
equally well on real-world datasets. Moreover, the research has rarely
explored vision-based tasks such as pose estimation, activity
recognition in videos, generative models, vision-language tasks and
real-time vision applications. This gap between published literature
for NAS and their performance on real-world datasets/applications is
yet to be addressed. The aim of this workshop is to advocate NAS for
in-the-wild computer vision across this wide range of tasks and
potentially across a range of computing platforms.

The workshop scope includes (but is not limited to):
- Neural architecture search (NAS)
- Challenges in using NAS and/or hyperparameter optimization (HPO) for
  real-world unconstrained datasets and applications
- Application of NAS/HPO in real-time computer vision applications
- Application of NAS/HPO beyond image classification and object detection
- Meta learning and transfer learning for computer vision
- Learning to learn for computer vision.

Paper Submission Deadline: January 6, 2020, 11:59:59 Pacific Standard Time.