Point-VOS: Pointing Up Video Object Segmentation

IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, pp. 22217–22226

Abstract

Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object mask annotations both during training and testing. This requires time-consuming and costly video annotation mechanisms. We propose a novel Point-VOS task with a spatio-temporaliy sparse point-wise annotation scheme that substantially re-duces the annotation effort. We apply our annotation scheme to two large-scale video datasets with text descriptions and annotate over 19M points across 133K objects in 32K videos. Based on our annotations, we propose a new Point-VOS benchmark, and a corresponding point-based training mechanism, which we use to establish strong base-line results. We show that existing VOS methods can easily be adapted to leverage our point annotations during training, and can achieve results close to the fully-supervised performance when trained on pseudo-masks generated from these points. In addition, we show that our data can be used to improve models that connect vision and language, by evaluating it on the Video Narrative Grounding (VNG) task. We will make our code and annotations available at https://pointvos.github.io.

Authors 4

1. Sabarinath Mahadevan Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

2. Idil Esen Zulfikar Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

3. Paul Voigtlaender

   Google (United States)

   Affiliation as printed

   Google Research

4. Bastian Leibe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany