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The DAVIS dataset has moved to maintenance mode. As a result, we do not plan to organize any further challenges and we will no longer update the benchmark leaderboards. However, we have integrated the existing results into paperswithcode. You can enter the results for you latest paper in the corresponding task webpage:
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Also, we will continue running the evaluation servers in Codalab. We would like to thank the community for taking part in the challanges and we encourage everyone to keep using the datasets for video object segmentation or any other task!
Latest news!
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- Detail for the 2020 Challenge have been published!
- Codalab sites to evaluate test-dev for DAVIS 2017 Semi-Supervised and Unsupervised are up again!
- Train, val, test-dev and test-challenge sets for the DAVIS 2017 Unsupervised dataset available!
Datasets
- DAVIS 2016: In each video sequence a single instance is annotated.
- DAVIS 2017 Semi-supervised: In each video sequence multiple instances are annotated.
- DAVIS 2017 Unsupervised: In each video sequence multiple instances are annotated.
Semi-supervised and Unsupervised refer to the level of human interaction at test time, not during the training phase. In Semi-supervised, better called human guided, the segmentation mask for the objects of interest is provided in the first frame. In Unsupervised, better called human non-guided, no human input is provided.
Publications
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The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
S. Caelles, J. Pont-Tuset, F. Perazzi, A. Montes, K.-K. Maninis, and L. Van Gool
arXiv:1905.00737, 2019
[PDF] [BibTex]
The 2018 DAVIS Challenge on Video Object Segmentation
S. Caelles, A. Montes, K.-K. Maninis, Y. Chen, L. Van Gool, F. Perazzi, and J. Pont-Tuset
arXiv:1803.00557, 2018
[PDF] [BibTex]
The 2017 DAVIS Challenge on Video Object Segmentation
J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool
arXiv:1704.00675, 2017
[PDF] [BibTex]
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung
Computer Vision and Pattern Recognition (CVPR) 2016
[PDF] [Supplemental] [BibTex]
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Please cite the relevant papers in your publications if DAVIS helps your research.Preview of the Annotations (Train and Val DAVIS 2017 Semi-supervised)
Benchmark State-of-the-Art
Display the evaluation of the current State-of-the-Art segmentation tecniques in DAVIS; using the three presented measures in our work.
Explore State-of-the-Art Results
Visualize the segmentation results for all state-of-the-Art techniques on all DAVIS 2016 images, right from your browser.
Downloads
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Download the DAVIS images and annotations, pre-computed results from all techniques, and the code to reproduce the evaluation.
Contributions from the community
These are the papers and projects in which the community has augmented our datasets:
- Referring expression annotations for DAVIS 2016 and DAVIS 2017
It contains the referring expression given the first frame to two annotators in DAVIS 2016 and DAVIS 2017. Moreover, it contains the referring expression given the whole video to two annotators in DAVIS 2017.Video Object Segmentation with Language Referring Expressions
A. Khoreva, A. Rohrbach, and B. Schiele
ACCV 2018
[Website] [BibTex]
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- Human eye fixation for DAVIS 2016.
It contains human eye fixation data captured with professional equipment by twenty participants for all the frames in DAVIS 2016 Train and Val.Learning Unsupervised Video Object Segmentation through Visual Attention
W. Wang, H. Song, S. Zhao, J. Shen, S. Zhao, S.C.H. Hoi, H. Ling
CVPR 2019
[Website] [BibTex]
- Shadow annotations for DAVIS 2016.
It contains the annotation of object shadows in 29 videos of DAVIS 2016.Temporally Coherent Completion of Dynamic Video
J. Huang, S. B. Kang, N. Ahuja, J. Kopf
ACM 2016
[Website] [BibTex]