Uncertainty-Aware Few-Shot Class-Incremental Learning

In a real-world setting, machine needs to continuously recognize new categories without forgetting. However, the number of new categories may be small. For some difficult categories, even humans cannot recognize only based on few-shot examples. To address the above issues, an innovative uncertainty-aware few-shot class incremental learning method (UACL) is proposed, which allows the model to continuously recognize new classes with few-shot examples and identify the classes it cannot recognize currently. Besides, in order to imitate the cognitive way of human beings and improve the continuous representation ability, we propose a pseudo-incremental task construction mechanism based on uncertainty estimation, where the machine learn to recognize from simple to difficult. Further, a large-scale pre-training model is used as an expert system to guide the model to recognize difficult classes. We evaluate our method on three popular benchmark datasets, showing that UACL is state-of-the-art.