Interactive Image Segmentation (IIS) has emerged as a promising technique for
decreasing annotation time. Substantial progress has been made in pre- and
post-processing for IIS, but the critical issue of interaction ambiguity
notably hindering segmentation quality, has been under-researched. To address
this, we introduce AdaptiveClick -- a clicks-aware transformer incorporating an
adaptive focal loss, which tackles annotation inconsistencies with tools for
mask- and pixel-level ambiguity resolution. To the best of our knowledge,
AdaptiveClick is the first transformer-based, mask-adaptive segmentation
framework for IIS. The key ingredient of our method is the Clicks-aware
Mask-adaptive Transformer Decoder (CAMD), which enhances the interaction
between clicks and image features. Additionally, AdaptiveClick enables
pixel-adaptive differentiation of hard and easy samples in the decision space,
independent of their varying distributions. This is primarily achieved by
optimizing a generalized Adaptive Focal Loss (AFL) with a theoretical
guarantee, where two adaptive coefficients control the ratio of gradient values
for hard and easy pixels. Our analysis reveals that the commonly used Focal and
BCE losses can be considered special cases of the proposed AFL loss. With a
plain ViT backbone, extensive experimental results on nine datasets demonstrate
the superiority of AdaptiveClick compared to state-of-the-art methods. Code
will be publicly available at this https URL

AdaptiveClick 是一种基于 Transformer 的、面向 Interactive Image Segmentation 的 mask-adaptive 分割框架，它通过引入 CAMD 和 Adaptive Focal Loss 解决了数据标注的不一致性和困难样本问题，实验结果显示其在 9 个数据集上超越了现有的其他方法。