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
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.