GyGO: an E-commerce Video Object Segmentation Dataset by Visualead

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Introduction

GyGO (pronounced jai-go) is a video object segmentation dataset focused on e-commerce. It is currently comprised of 131 training and 24 validation sequences, along with their corresponding high quality annotations.

On the one hand, the sequences are quite simple in that they are virtually devoid of occlusions, fast motions or many of the other complexity inducing attributes mentioned above. On the other hand, the objects in these videos vary wildly in their semantic categories: toys, cloths, models and office fluff.

Each sequence is 1-10 seconds long and has been captured by a handheld smartphone camera. It was then decimated to ~5 fps in post-production and annotated by a non-trivial aggregation of Amazon Mechanical Turk workers.

We release the dataset publicly with two goals in mind:

• There is a severe lack of data in the space of video object segmentation at the moment. With only hundreds of annotated videos, we believe every contribution has the potential to increase performance. In our internal (soon to be published) analysis we have shown that a joint training on the GyGO and DAVIS datasets yields improved inference results. [todo: confirm]
• To promote a more open, sharing culture and encourage other researchers to join our efforts :) The DAVIS dataset and the research ecosystem that grew it have been massively useful for us. We hope the community will find our datasets useful as well.

Teaser:

Download

https://gygox-assets.oss-us-east-1.aliyuncs.com/gygo-dataset.tar.gz

Folder structure

.
├── JPEGImages # folders containing RGB videos
|   ├── 480p
|   └── original
├── Annotations # folders containing binary annotations
|   ├── 480p
|   └── original
└── ImageSets
    ├── Train.txt # contains a list of all the sequences for training
    ├── trainval.txt # contains ALL the sequences for training and for validation    
    └── val.txt # contains a list of all the for validation

Benchmarks

TBD

Links

Video Object Segmentation — The Basics

A Meta-analysis of DAVIS-2017 Video Object Segmentation Challenge

The GyGO dataset was drew a lot of inspiration from:

DAVIS Challenge (video object segmentation datasets)

Contributors

Itamar Friedman, Ilan Chemla, Eddie Smolyansky, Maxim Stepanov, Irina Afanasyeva, Gilad Sharir, Sagi Nadir, Sagi Rorlich