Self-Trained Proposal Networks for the Open World

Deep learning-based object proposal methods have enabled significant advances in many computer vision pipelines. However, current state-of-the-art proposal networks use a closed-world assumption, meaning they are only trained to detect instances of the training classes while treating every other region as background. This style of solution fails to provide high recall on out-of-distribution objects, rendering it inadequate for use in realistic open-world applications where novel object categories of interest may be observed. To better detect all objects, we propose a classification-free Self-Trained Proposal Network (STPN) that leverages a novel self-training optimization strategy combined with dynamically weighted loss functions that account for challenges such as class imbalance and pseudo-label uncertainty. Not only is our model designed to excel in existing optimistic open-world benchmarks, but also in challenging operating environments where there is significant label bias. To showcase this, we devise two challenges to test the generalization of proposal models when the training data contains (1) less diversity within the labeled classes, and (2) fewer labeled instances. Our results show that STPN achieves state-of-the-art novel object generalization on all tasks.