Workshop on Neural Architecture Search for Computer Vision in the Wild @ WACV 2020
NASFW 2020
- URL: https://nasfw20.github.io/
- Event Date: 2020-03-02 ~ 2020-03-05
- Submission Date: 2019-12-20
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: December 20, 2019, 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/
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: December 20, 2019, 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/
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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