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 https://github.com/lab206/AdaptiveClick.

翻译：[translated abstract in Chinese] 交互式图像分割（IIS）已成为一种可减少标注时间的前沿技术。现有研究在IIS的预处理和后处理方面取得了显著进展，但交互歧义这一严重影响分割质量的关键问题仍未得到充分探索。为此，我们提出AdaptiveClick——一种融合自适应焦点损失的点击感知Transformer，通过掩码级与像素级歧义消解工具应对标注不一致性。据我们所知，AdaptiveClick是首个基于Transformer的掩码自适应IIS分割框架。其核心组件为点击感知掩码自适应Transformer解码器（CAMD），可增强点击操作与图像特征间的交互。此外，AdaptiveClick能在决策空间中独立于样本分布差异，实现难易样本的像素级自适应区分。这一功能主要通过优化具有理论保证的广义自适应焦点损失（AFL）实现，其中两个自适应系数控制难易像素的梯度值比率。分析表明，广泛使用的焦点损失与交叉熵损失可视为所提AFL损失的特殊形式。基于朴素ViT骨干网络，在九个数据集上的大量实验结果证明了AdaptiveClick相比现有最优方法的优越性。代码将开源于https://github.com/lab206/AdaptiveClick。