Unsupervised Out-of-Distribution Object Detection via PCA-Driven Dynamic Prototype Enhancement

To promote the application of object detectors in real scenes, out-of-distribution object detection (OOD-OD) is proposed to distinguish whether detected objects belong to the ones that are unseen during training or not. One of the key challenges is that detectors lack unknown data for supervision, and as a result, can produce overconfident detection results on OOD data. Thus, this task requires to synthesize OOD data for training, which achieves the goal of enhancing the ability of localizing and discriminating OOD objects. In this paper, we propose a novel method, i.e., PCA-Driven dynamic prototype enhancement, to explore exploiting Principal Component Analysis (PCA) to extract simulative OOD data for training and obtain dynamic prototypes that are related to the current input and are helpful for boosting the discrimination ability. Concretely, the last few principal components of the backbone features are utilized to calculate an OOD map that involves plentiful information that deviates from the correlation distribution of the input. The OOD map is further used to extract simulative OOD data for training, which alleviates the impact of lacking unknown data. Besides, for in-distribution (ID) data, the category-level semantic information of objects between the backbone features and the high-level features should be kept consistent. To this end, we utilize the residual principal components to extract dynamic prototypes that reflect the semantic information of the current backbone features. Next, we define a contrastive loss to leverage these prototypes to enlarge the semantic gap between the simulative OOD data and the features from the residual principal components, which improves the ability of discriminating OOD objects. In the experiments, we separately verify our method on OOD-OD and incremental object detection. The significant performance gains demonstrate the superiorities of our method.