SLViT: Scale-Wise Language-Guided Vision Transformer for Referring Image Segmentation
Shuyi Ouyang, Hongyi Wang, Shiao Xie, Ziwei Niu, Ruofeng Tong, Yen‐Wei Chen, Lanfen Lin
Abstract
Referring image segmentation aims to segment an object out of an image via a specific language expression. The main concept is establishing global visual-linguistic relationships to locate the object and identify boundaries using details of the image. Recently, various Transformer-based techniques have been proposed to efficiently leverage long-range cross-modal dependencies, enhancing performance for referring segmentation. However, existing methods consider visual feature extraction and cross-modal fusion separately, resulting in insufficient visual-linguistic alignment in semantic space. In addition, they employ sequential structures and hence lack multi-scale information interaction. To address these limitations, we propose a Scale-Wise Language-Guided Vision Transformer (SLViT) with two appealing designs: (1) Language-Guided Multi-Scale Fusion Attention, a novel attention mechanism module for extracting rich local visual information and modeling global visual-linguistic relationships in an integrated manner. (2) An Uncertain Region Cross-Scale Enhancement module that can identify regions of high uncertainty using linguistic features and refine them via aggregated multi-scale features. We have evaluated our method on three benchmark datasets. The experimental results demonstrate that SLViT surpasses state-of-the-art methods with lower computational cost. The code is publicly available at: https://github.com/NaturalKnight/SLViT.