MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation

Interactive segmentation has gained significant attention due to its applications in human-computer interaction and data annotation. To address the challenge of target scale variations in interactive segmentation, we propose a novel multi-scale token fusion algorithm. This algorithm selectively fuses only the most important tokens, enabling the model to better capture multi-scale characteristics in important regions. To further enhance the robustness of multi-scale token selection, we introduce a token learning algorithm based on contrastive loss. This algorithm fully utilizes the discriminative information between target and background multi-scale tokens, effectively improving the quality of selected tokens. Extensive benchmark testing demonstrates the effectiveness of our approach in addressing multi-scale issues. The code and data have been made publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hahamyt/mst</uri>.