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.

DynaMITe 是一种更有效率的交互式分割网络方法，采用 Transform 器解码器将用户交互表示为时空查询，从而在单个迭代中分割多个目标实例，不需要重新计算图像特征，可在较少的交互次数下在单个图像中分割多个实例，并在多个现有互动分割基准测试中以及我们提出的新的多实例基准测试中实现了最新的结果。