Human-in-the-Loop Video Semantic Segmentation Auto-Annotation

Abstract

Accurate per-pixel semantic class annotations of the entire video are crucial for designing and evaluating video semantic segmentation algorithms. However, the annotations are usually limited to a small subset of the video frames due to the high annotation cost and limited budget in practice. In this paper, we propose a novel human-in-the-loop framework called HVSA to generate semantic segmentation annotations for the entire video using only a small annotation budget. Our method alternates between active sample selection and test-time fine-tuning algorithms until annotation quality is satisfied. In particular, the active sample selection algorithm picks the most important samples to get manual annotations, where the sample can be a video frame, a rectangle, or even a super-pixel. Further, the test-time fine-tuning algorithm propagates the manual annotations of selected samples to the entire video. Real-world experiments show that our method generates highly accurate and consistent semantic segmentation annotations while simultaneously enjoys significantly small annotation cost.