  How can we segment varying numbers of objects where each specific object
represents its own separate class? To make the problem even more realistic, how
can we add and delete classes on the fly without retraining or fine-tuning?
This is the case of robotic applications where no datasets of the objects exist
or application that includes thousands of objects (E.g., in logistics) where it
is impossible to train a single model to learn all of the objects. Most current
research on object segmentation for robotic grasping focuses on class-level
object segmentation (E.g., box, cup, bottle), closed sets (specific objects of
a dataset; for example, YCB dataset), or deep learning-based template matching.
In this work, we are interested in open sets where the number of classes is
unknown, varying, and without pre-knowledge about the objects' types. We
consider each specific object as its own separate class. Our goal is to develop
an object detector that requires no fine-tuning and can add any object as a
class just by capturing a few images of the object. Our main idea is to break
the segmentation pipelines into two steps by combining unseen object
segmentation networks cascaded by class-adaptive classifiers. We evaluate our
class-adaptive object detector on unseen datasets and compare it to a trained
Mask R-CNN on those datasets. The results show that the performance varies from
practical to unsuitable depending on the environment setup and the objects
being handled. The code is available in our DoUnseen library repository.
