Object Level Annotations Engine for OmniArt

The OmniArt dataset now features an object level annotation tool

Object play a key role in understanding what is happening in an image. Using our object level annotation tool we have annotated 5000 data samples so far for objects that are not so common or are not easy to translate from real world images. For common objects that do not change their appearance significantly we can use the knowledge obtained from the real world. For example, bicycles have had the same primitive parts for a while now - two wheels, a frame, a seat and a power transfer mechanism from the feet to the wheels.

Chances are if a model is able to detect bicycles in the real world, it can do so in the artistic realm as well.

We are working hard on improving the quality of the dataset and providing extensive information on the entities contained within.

If you use this dataset in your research, make sure to cite this paper:

@article{  strezoski2017omniart,
           title={OmniArt: Multi-task Deep Learning for Artistic Data Analysis},
           author={Strezoski, Gjorgji and Worring, Marcel},
           journal={arXiv preprint arXiv:1708.00684},
           year={2017}
        }

and we are interested in your feedback.

 
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