Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
This addresses false positives for domain adaptive pedestrian detection, offering an incremental improvement by adapting existing methods to unsupervised settings.
The paper tackles false positives in domain adaptive pedestrian detection by modeling detection as a ranking task and transforming false positive suppression into an unsupervised box re-ranking problem, achieving state-of-the-art results on cross-domain datasets and general benchmarks.
False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countless target domains. Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way. In this paper, we model an object detection task into a ranking task among positive and negative boxes innovatively, and thus transform a false positive suppression problem into a box re-ranking problem elegantly, which makes it feasible to solve without manual annotation. An attached problem during box re-ranking appears that no labeled validation data is available for cherrypicking. Considering we aim to keep the detection of true positive unchanged, we propose box number alignment, a self-supervised evaluation metric, to prevent the optimized model from capacity degeneration. Extensive experiments conducted on cross-domain pedestrian detection datasets have demonstrated the effectiveness of our proposed framework. Furthermore, the extension to two general unsupervised domain adaptive object detection benchmarks also supports our superiority to other state-of-the-arts.