  Most state-of-the-art instance segmentation methods rely on large amounts of
pixel-precise ground-truth annotations for training, which are expensive to
create. Interactive segmentation networks help generate such annotations based
on an image and the corresponding user interactions such as clicks. Existing
methods for this task can only process a single instance at a time and each
user interaction requires a full forward pass through the entire deep network.
We introduce a more efficient approach, called DynaMITe, in which we represent
user interactions as spatio-temporal queries to a Transformer decoder with a
potential to segment multiple object instances in a single iteration. Our
architecture also alleviates any need to re-compute image features during
refinement, and requires fewer interactions for segmenting multiple instances
in a single image when compared to other methods. DynaMITe achieves
state-of-the-art results on multiple existing interactive segmentation
benchmarks, and also on the new multi-instance benchmark that we propose in
this paper.
