We introduce a Unified Model for Human-Centric Perceptions (UniHCP), which can easily handle multiple distinctly defined human-centric tasks simultaneously, be trained at scale and obtains a series of SOTA performances over a wide spectrum of human-centric benchmarks.
Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and metaverse. It is desirable to have a general pretrain model for versatile human-centric …
We introduce Fast-MoCo, a simple yet effective self-supervised learning method that boosts the training speed of the momentum-based contrastive learning with combinatorial patches.
We introduce a neural architecture search scheme called NSE. It can progressively accommodate new search space while maintaining the previously obtained knowledge.
We proposed a model and a dataset for accurate anime line art colorization. This model improved the visual result over the previously proposed methods.