Causality-Inspired Debiasing Learning for Open World Object Detection

Open world object detection (OWOD) aims to identify both known instances of trained classes and unknown ones. Despite recent advancements, existing methods exhibit a detection bias towards known classes, as detectors are exclusively trained under the supervision of known classes. To address this problem, we construct a causal graph to scrutinize OWOD from a causal perspective, revealing that the bias problem primarily arises due to the confounding effect of known classes, and the causality between unknown objects and their predictions learned by the detector is weak. Therefore, we propose a causality-inspired debiasing framework for OWOD, aiming to bolster the performance of OWOD models by eliminating confounders and encouraging appropriate features. Specifically, a semantic causal intervention module is proposed to remove the confounding effect from known classes to unknown features, which introduces the known semantics to interact fairly with all unknown features through backdoor adjustment. Moreover, an unknown causality enhancement module is employed to enhance the causality of unknown objects and their predictions acquired by the model, which imposes constraints for different unknown classes in feature space with the contrastive learning paradigm from the perspective of intervention effect. Extensive experiments conducted on the commonly-used OWOD benchmarks demonstrate that our framework consistently yields superior results on unknown classes compared with state-of-the-art methods by a large margin (+25.0% UD-Pre, +10.2% Recall on unknown classes) and even better on known classes (+1.4% mAP on known classes).