An Open Vocabulary Semantic Segmentation Model SAN Integrating Multi Scale Channel Attention
With the development of visual language models,open vocabulary methods have been widely used in identifying categories outside the annotated label.Compared with the weakly supervised and zero sample method,the open vocabulary method is proved to be more versatile and effective.The goal of this study is to improve the lightweight model SAN for open vocabulary segmentation,which introduces a feature fusion mechanism AFF based on multi scale channel attention to improve the model,and improve the dual branch feature fusion method in the original SAN structure.Then,the improved algorithm is evaluated based on multiple semantic segmentation benchmarks,and the results show that the model performance has certain improvement with almost no change in the number of parameters.This improvement plan will help simplify future research on open vocabulary semantic segmentation.
open vocabularysemantic segmentationSANCLIPmulti scale channel attention
国家科技期刊平台
NSTL
万方数据
