Click-point-based interactive segmentation has received widespread attention
due to its efficiency. However, it's hard for existing algorithms to obtain
precise and robust responses after multiple clicks. In this case, the
segmentation results tend to have little change or are even worse than before.
To improve the robustness of the response, we propose a structured click intent
model based on graph neural networks, which adaptively obtains graph nodes via
the global similarity of user-clicked Transformer tokens. Then the graph nodes
will be aggregated to obtain structured interaction features. Finally, the dual
cross-attention will be used to inject structured interaction features into
vision Transformer features, thereby enhancing the control of clicks over
segmentation results. Extensive experiments demonstrated the proposed algorithm
can serve as a general structure in improving Transformer-based interactive
segmenta?tion performance. The code and data will be released at
this https URL

我们提出了一种基于图神经网络的结构点击意图模型，通过全局相似性获得用户点击的 Transformer token，然后聚合图节点以获得结构交互特征，最后使用双重交互注意力将结构交互特征注入视觉 Transformer 特征，从而增强点击对分割结果的控制，实验证明该算法能够提高基于 Transformer 的交互分割性能。