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Evaluation Framework for DAVIS Interactive Segmentation

Project description

DAVIS Interactive Evaluation Framework

|Travis| |Codecov branch| |GPLv3 license|

This is a framework to evaluate interactive segmentation models over the
`DAVIS <>`__ dataset. The code aims
to provide an easy-to-use interface to test and validate interactive
segmentation models.

This is the tool that will be used to evaluate the DAVIS Challenge on
Video Object Segmentation 2018 on the interactive track. More info about
the challenge on the
`website <>`__.

**Note**: code still under development.

DAVIS Scribbles

On previous DAVIS Challenge the task consisted on object segmentation in
a semisupervised manner. The input given was the ground truth mask of
the first frame. For DAVIS interactive challenge we change the
annotation to scribbles which can be annotated faster by humans.

The interactive annotation and segmentation consist on a iterative loop
which is going to be evaluated as follows:

- On the first iteration, a human annotated scribble will be provided
to the segmentation model. All the scribbles are annotated over the
DAVIS dataset and the objects annotated will be the same as the
ground truth masks. **Note**: the annotated frame can be any of the
sequence as the humans where asked to annotate the frames that found
most relevant and meaningfull to annotate.
- During the rest of the iterations, once the predicted masks have been
submitted, an automated scribble is generated simulating human
annotation. The new annotation will be performed on a single frame
and this frame will be chosen as the worst on the evaluation metric.

**Evaluation**: For now, the evaluation metric will be the Jaccard
similarity :math:`\mathcal{J}`.


Please cite both papers in your publications if DAVIS or this code helps
your research.

.. code:: tex

author = {Sergi Caelles and Alberto Montes and Kevis-Kokitsi Maninis and Yuhua Chen and Luc {Van Gool} and Federico Perazzi and Jordi Pont-Tuset},
title = {The 2018 DAVIS Challenge on Video Object Segmentation},
journal = {arXiv:1803.00557},
year = {2018}

.. code:: latex

author = {F. Perazzi and J. Pont-Tuset and B. McWilliams and L. {Van Gool} and M. Gross and A. Sorkine-Hornung},
title = {A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation},
booktitle = {Computer Vision and Pattern Recognition},
year = {2016}

.. |Travis| image::
.. |Codecov branch| image::
.. |GPLv3 license| image::

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