Semi-supervised semantic segmentation (S4) has shown significant promise in reducing the burden of labor-intensive data annotation. However, existing methods mainly rely on pixel-level information, neglecting the strong region consistency inherent in …
Recently, semi-supervised learning attracts extensive interests in the field of object detection, since it is beneficial for alleviating the label annotation burden. Existing methods generates unsatisfactory pseudo labels and have imperfect …
Most of the recent research in semi-supervised object detection follows the pseudo-labeling paradigm evolved from the semi-supervised image classification task. However, the training paradigm of the two-stage object detector inevitably makes the …
In the semi-supervised object detection task, due to the scarcity of labeled data and the diversity and complexity of objects to be detected, the quality of pseudo-labels generated by existing methods for unlabeled data is relatively low, which …
Open-set semi-supervised learning~(open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. While the mainstream technique seeks to completely filter out the OOD …