HSIC-based Moving Weight Averaging for Few-Shot Open-Set Object Detection

We study the problem of few-shot open-set object detection (FOOD), whose goal is to quickly adapt a model to a small set of labeled samples and reject unknown class samples. Recent works usually use the weight sparsification for unknown rejection, but due to the lack of tailored considerations for data-scarce scenarios, the performance is not satisfactory. In this work, we solve the challenging few-shot open-set object detection problems from three aspects. First, different from previous pseudo-unknown sample mining methods, we employ the evidential uncertainty estimated by the Dirichlet distribution of probability to mine the pseudo-unknown samples from the foreground and background proposal space. Second, based on the statistical analysis between the number of pseudo-unknown samples and the Intersection over Union (IoU), we propose an IoU-aware unknown objective, which sharps the unknown decision boundary by considering the localization quality. Third, to suppress the over-fitting problem and improve the model's generalization ability for unknown rejection, we propose the HSIC-based (Hilbert-Schmidt Independence Criterion) moving weight averaging to update the weights of classification and regression heads, which considers the degree of independence between the current weights and previous weights stored in the long-term memory banks. We compare our method with several state-of-the-art methods and observe that our method improves the mean recall of unknown classes by 12.87% across all shots in the VOC-COCO dataset settings. Our code is available at https://github.com/binyisu/food.