Upcycling Models under Domain and Category Shift

Sanqing Qu, Tianpei Zou, Florian Röhrbein, Cewu Lu, Guang Chen^*, Dacheng Tao , Changjun Jiang

IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023

[Paper] [Code] [Chinese Version]

Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. To address this, in this paper, we explore the Source-free Universal Domain Adaptation (SF-UniDA). SF-UniDA is appealing in view that universal model adaptation can be resolved only on the basis of a standard pre-trained closed-set model, i.e., without source raw data and dedicated model architecture. To achieve this, we develop a generic global and local clustering technique (GLC). GLC equips with an inovative one-vs-all global pseudo-labeling strategy to realize "known" and "unknown" data samples separation under various category-shift. Remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8% on the VisDA benchmark.
The illustration of the GLC framework is shown below.

Demo video