Better (pseudo-)labels for semi-supervised instance segmentation

Francois Porcher, camille couprie, Marc Szafraniec, Jakob Verbeek

Keywords: semi-supervised learning, instance segmentation, label smoothing, model calibration

TL;DR: Improved instance segmentation on LVIS using semi-supervised teacher-student distillation approach, using non-uniform label-smoothing and model calibration techniques

Abstract: Despite the availability of large datasets for tasks like image classification and image-text alignment, labeled data for more complex recognition tasks, such as detection and segmentation, is less abundant. In particular, for instance segmentation annotations are time-consuming to produce, and the distribution of instances is often highly skewed across classes. While semi-supervised teacher-student distillation methods show promise in leveraging vast amounts of unlabeled data, they suffer from miscalibration, resulting in overconfidence in frequently represented classes and underconfidence in rarer ones. Additionally, these methods encounter difficulties in efficiently learning from a limited set of examples. We introduce a dual-strategy to enhance the teacher model's training process, substantially improving the performance on few-shot learning. Secondly, we propose a calibration correction mechanism that that enables the student model to correct the teacher's calibration errors. Using our approach, we observed marked improvements over a state-of-the-art supervised baseline performance on the LVIS dataset, with an increase of 2.8% in average precision (AP) and 10.3% gain in AP for rare classes.

Submission Number: 20