ReMamber: Referring Image Segmentation with Mamba Twister

arXiv:2403.17839v1 Announce Type: new
Abstract: Referring Image Segmentation (RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cost makes it resource-consuming in capturing long-range visual-language dependencies. Fortunately, Mamba addresses this with efficient linear complexity in processing. However, directly applying Mamba to multi-modal interactions presents challenges, primarily due to inadequate channel interactions for the effective fusion of multi-modal data. In this paper, we propose ReMamber, a novel RIS architecture that integrates the …

abstract arxiv complexity computation cost cs.ai cs.cv dependencies however image interactions interpretation language linear mamba modal multi-modal processing segmentation success tasks transformers type visual