  Humans can easily distinguish the known and unknown categories and can
recognize the unknown object by learning it once instead of repeating it many
times without forgetting the learned object. Hence, we aim to make deep
learning models simulate the way people learn. We refer to such a learning
manner as OnLine Open World Object Detection(OLOWOD). Existing OWOD approaches
pay more attention to the identification of unknown categories, while the
incremental learning part is also very important. Besides, some neuroscience
research shows that specific noises allow the brain to form new connections and
neural pathways which may improve learning speed and efficiency. In this paper,
we take the dual-level information of old samples as perturbations on new
samples to make the model good at learning new knowledge without forgetting the
old knowledge. Therefore, we propose a simple plug-and-play method, called
Brain-inspired Streaming Dual-level Perturbations(BSDP), to solve the OLOWOD
problem. Specifically, (1) we first calculate the prototypes of previous
categories and use the distance between samples and the prototypes as the
sample selecting strategy to choose old samples for replay; (2) then take the
prototypes as the streaming feature-level perturbations of new samples, so as
to improve the plasticity of the model through revisiting the old knowledge;
(3) and also use the distribution of the features of the old category samples
to generate adversarial data in the form of streams as the data-level
perturbations to enhance the robustness of the model to new categories. We
empirically evaluate BSDP on PASCAL VOC and MS-COCO, and the excellent results
demonstrate the promising performance of our proposed method and learning
manner.
