Open-vocabulary segmentation (OVS) has gained attention for its ability to recognize a broader range of classes. However, OVS models show significant performance drops when applied to unseen domains beyond the previous training dataset. Fine-tuning these models on new datasets can improve performance, but often leads to the catastrophic forgetting of previously learned knowledge. To address this issue, we propose a method that allows OVS models to learn information from new domains while preserving prior knowledge. Our approach begins by evaluating the input sample's proximity to multiple domains, using precomputed multivariate normal distributions for each domain. Based on this prediction, we dynamically interpolate between the weights of the pre-trained decoder and the fine-tuned decoders. Extensive experiments demonstrate that this approach allows OVS models to adapt to new domains while maintaining performance on the previous training dataset. The source code is available at https://github.com/dongjunhwang/dwi.

本研究针对开放词汇分割（OVS）模型在新领域中的显著性能下降问题，提出了一种保持先前知识的同时从新领域学习信息的方法。通过评估输入样本与多个领域的接近度，采用动态插值机制，提高了模型在新领域的适应性，并保持了在先前训练数据集上的性能。

克服开放词汇分割中的领域限制