Semi-supervised Open-World Object Detection
However, the current OWOD formulation heavily relies on the external human oracle for knowledge input during the incremental learning stages. Such reliance on run-time makes this formulation less realistic in a real-world deployment. To address this, we introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD), that reduces the annotation cost by casting the incremental learning stages of OWOD in a semi-supervised manner. We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Therefore, we introduce a novel SS-OWOD detector, named SS-OWFormer, that utilizes a feature-alignment scheme to better align the object query representations between the original and augmented images to leverage the large unlabeled and few labeled data. We further introduce a pseudo-labeling scheme for unknown detection that exploits the inherent capability of decoder object queries to capture object-specific information. On the COCO dataset, our SS-OWFormer using only 50\% of the labeled data achieves detection performance that is on par to the state-of-the-art (SOTA) OWOD detector using all the 100\% of labeled data. Further, our SS-OWFormer achieves an absolute gain of 4.8\% in unknown recall over the SOTA OWOD detector. Lastly, we demonstrate the effectiveness of our SS-OWOD problem setting and approach for remote sensing object detection on DOTA splits with performance comparisons to the baseline.
S.S. Mullappilly, "Semi-supervised Open-World Object Detection", M.S. Thesis, Computer Vision, MBZUAI, Abu Dhabi, UAE, 2023.