HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images

IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, pp. 3012–3021

Abstract

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. With out any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations. Source code: https://github.com/Ali2500/HODOR.

Authors 5

1. Ali Athar Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

   RWTH Aachen University, Germany

2. Jonathon Luiten Aachen

   Carnegie Mellon University · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

   Carnegie Mellon University, USA

   RWTH Aachen University, Germany

3. Alexander Hermans Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

   RWTH Aachen University, Germany

4. Deva Ramanan

   Carnegie Mellon University

   Affiliation as printed

   Carnegie Mellon University,USA

   Carnegie Mellon University, USA

5. Bastian Leibe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

   RWTH Aachen University, Germany