  Recent works on click-based interactive segmentation have demonstrated
state-of-the-art results by using various inference-time optimization schemes.
These methods are considerably more computationally expensive compared to
feedforward approaches, as they require performing backward passes through a
network during inference and are hard to deploy on mobile frameworks that
usually support only forward passes. In this paper, we extensively evaluate
various design choices for interactive segmentation and discover that new
state-of-the-art results can be obtained without any additional optimization
schemes. Thus, we propose a simple feedforward model for click-based
interactive segmentation that employs the segmentation masks from previous
steps. It allows not only to segment an entirely new object, but also to start
with an external mask and correct it. When analyzing the performance of models
trained on different datasets, we observe that the choice of a training dataset
greatly impacts the quality of interactive segmentation. We find that the
models trained on a combination of COCO and LVIS with diverse and high-quality
annotations show performance superior to all existing models. The code and
trained models are available at
https://github.com/saic-vul/ritm_interactive_segmentation.
