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STEm-Seg: Spatio-Temporal Embeddings for Instance Segmentation in Videos

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12356))

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Abstract

Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by-detection paradigm and model a video clip as a sequence of images. Multiple networks are used to detect objects in individual frames, and then associate these detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos. In particular, we model a video clip as a single 3D spatio-temporal volume, and propose a novel approach that segments and tracks instances across space and time in a single stage. Our problem formulation is centered around the idea of spatio-temporal embeddings which are trained to cluster pixels belonging to a specific object instance over an entire video clip. To this end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters required to cluster these embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks. Code and models are available at https://github.com/sabarim/STEm-Seg.

A. Athar and S. Mahadevan—Equal contribution.

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Notes

  1. 1.

    This notation denotes element-wise addition between \(\textit{\textbf{e}}_i\) and \([x_i, y_i]\).

  2. 2.

    Results for various \(\phi (\cdot )\) on YT-VIS and KITTI-MOTS are given in supplementary.

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Acknowledgements

This project was funded, in parts, by ERC Consolidator Grant DeeVise (ERC-2017-COG-773161), EU project CROWDBOT (H2020-ICT-2017-779942) and the Humboldt Foundation through the Sofja Kovalevskaja Award. Computing resources for several experiments were granted by RWTH Aachen University under project ‘rwth0519’. We thank Sebastian Hennen for help with experiments and Francis Engelmann, Theodora Kontogianni, Paul Voigtlaender, Gulliem Brasó and Aysim Toker for helpful discussions.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Ali Athar, Sabarinath Mahadevan & Bastian Leibe

  2. Technical University of Munich, Munich, Germany

    Aljos̆a Os̆ep & Laura Leal-Taixé

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  1. Ali Athar
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  2. Sabarinath Mahadevan
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  3. Aljos̆a Os̆ep
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  4. Laura Leal-Taixé
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  5. Bastian Leibe
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Correspondence to Ali Athar , Sabarinath Mahadevan , Aljos̆a Os̆ep , Laura Leal-Taixé or Bastian Leibe .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Athar, A., Mahadevan, S., Os̆ep, A., Leal-Taixé, L., Leibe, B. (2020). STEm-Seg: Spatio-Temporal Embeddings for Instance Segmentation in Videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_10

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