  Deep neural networks achieve remarkable performance in many computer vision
tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection
approaches reuse neural network architectures designed for image classification
as the backbone, commonly pre-trained on ImageNet. However, performance gains
can be achieved by designing network architectures specifically for detection
and segmentation, as shown by recent neural architecture search (NAS) research
for detection and segmentation. One major challenge though is that ImageNet
pre-training of the search space representation (a.k.a. super network) or the
searched networks incurs huge computational cost. In this paper, we propose a
Fast Network Adaptation (FNA++) method, which can adapt both the architecture
and parameters of a seed network (e.g. an ImageNet pre-trained network) to
become a network with different depths, widths, or kernel sizes via a parameter
remapping technique, making it possible to use NAS for segmentation and
detection tasks a lot more efficiently. In our experiments, we apply FNA++ on
MobileNetV2 to obtain new networks for semantic segmentation, object detection,
and human pose estimation that clearly outperform existing networks designed
both manually and by NAS. We also implement FNA++ on ResNets and NAS networks,
which demonstrates a great generalization ability. The total computation cost
of FNA++ is significantly less than SOTA segmentation and detection NAS
approaches: 1737x less than DPC, 6.8x less than Auto-DeepLab, and 8.0x less
than DetNAS. A series of ablation studies are performed to demonstrate the
effectiveness, and detailed analysis is provided for more insights into the
working mechanism. Codes are available at https://github.com/JaminFong/FNA.
