In the field of Industrial Informatics, interactive segmentation has gained
significant attention for its application in human-computer interaction and
data annotation. Existing algorithms, however, face challenges in balancing the
segmentation accuracy between large and small targets, often leading to an
increased number of user interactions. To tackle this, a novel multi-scale
token adaptation algorithm, leveraging token similarity, has been devised to
enhance segmentation across varying target sizes. This algorithm utilizes a
differentiable top-k tokens selection mechanism, allowing for fewer tokens to
be used while maintaining efficient multi-scale token interaction. Furthermore,
a contrastive loss is introduced to better discriminate between target and
background tokens, improving the correctness and robustness of the tokens
similar to the target. Extensive benchmarking shows that the algorithm achieves
state-of-the-art (SOTA) performance compared to current methods. An interactive
demo and all reproducible codes will be released at
this https URL

通过多尺度令牌调整算法改善了交互式分割的准确度，以应对大目标和小目标间的准确度平衡挑战，并引入了对比损失以提高令牌的正确性和鲁棒性。与现有方法相比，该算法在绩效上达到了最新水平。发布了交互式演示和所有可复制的代码。