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.

Key Dates:

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

Notification to Authors: January 15, 2020.

Camera Ready Deadline: February 1, 2020, 11:59:59pm Pacific Standard Time.


Workshop website: https://nasfw20.github.io/