Gaia-universe: Everything is super-netify

Junran Peng, Qing Chang, haoran yin, Xingyuan Bu, Jiajun Sun, Lingxi Xie, XIAOPENG ZHANG, Qi Tian, Zhaoxiang Zhang

Abstract:

Pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language
processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency
constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task
requirements. we focus on two fundamental perception tasks (object detection and semantic segmentation) and present a complete
and flexible system named GAIA-Universe(GAIA), which could automatically and efficiently give birth to customized solutions
according to heterogeneous downstream needs through data union and super-net training. GAIA is capable of providing powerful
pre-trained weights and searching models that conform to downstream demands such as hardware constraints, computation
constraints, specified data domains, and telling relevant data for practitioners who have very few datapoints on their tasks. With GAIA,
we achieve promising results on COCO, Objects365, Open Images, BDD100k, and UODB which is a collection of datasets including
KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models
covering a wide range of latency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and bells. GAIA is released at
https://github.com/GAIA-vision.