Efficient and Transferable Open-Vocabulary Segmentation with Principled Model and Training Optimization

We propose a principled and transferable approach to efficiently process open-vocabulary segmentation tasks by introducing a transferable sparse backbone and a selective fine-tuning strategy, achieving superior performance-efficiency trade-offs.