Interactive image segmentation enables annotators to efficiently perform
pixel-level annotation for segmentation tasks. However, the existing
interactive segmentation pipeline suffers from inefficient computations of
interactive models because of the following two issues. First, annotators'
later click is based on models' feedback of annotators' former click. This
serial interaction is unable to utilize model's parallelism capabilities.
Second, the model has to repeatedly process the image, the annotator's current
click, and the model's feedback of the annotator's former clicks at each step
of interaction, resulting in redundant computations. For efficient computation,
we propose a method named InterFormer that follows a new pipeline to address
these issues. InterFormer extracts and preprocesses the computationally
time-consuming part i.e. image processing from the existing process.
Specifically, InterFormer employs a large vision transformer (ViT) on
high-performance devices to preprocess images in parallel, and then uses a
lightweight module called interactive multi-head self attention (I-MSA) for
interactive segmentation. Furthermore, the I-MSA module's deployment on
low-power devices extends the practical application of interactive
segmentation. The I-MSA module utilizes the preprocessed features to
efficiently response to the annotator inputs in real-time. The experiments on
several datasets demonstrate the effectiveness of InterFormer, which
outperforms previous interactive segmentation models in terms of computational
efficiency and segmentation quality, achieve real-time high-quality interactive
segmentation on CPU-only devices.

提出了一种名为 InterFormer 的方法，使用大型 ViT 并在高性能设备上运行，通过轻量级 I-MSA 模块进行互动式分割，实现了计算效率更高、分割质量更好的实时高质量交互式分割，可在仅有 CPU 的设备上执行。