{"type":"ScholarlyArticle","title":"SLViT: Scale-Wise Language-Guided Vision Transformer for Referring Image Segmentation","authors":[{"name":"Shuyi Ouyang","slug":"shuyi-ouyang","given":"Shuyi","family":"Ouyang","affiliation":"Zhejiang University"},{"name":"Hongyi Wang","slug":"hongyi-wang","given":"Hongyi","family":"Wang","affiliation":"Zhejiang University"},{"name":"Shiao Xie","slug":"shiao-xie","given":"Shiao","family":"Xie","affiliation":"Zhejiang University"},{"name":"Ziwei Niu","slug":"ziwei-niu","given":"Ziwei","family":"Niu","affiliation":"Zhejiang University"},{"name":"Ruofeng Tong","slug":"ruofeng-tong","given":"Ruofeng","family":"Tong","affiliation":"Zhejiang University; Zhejiang Lab"},{"name":"Yen-Wei Chen","slug":"yen-wei-chen","given":"Yen-Wei","family":"Chen","affiliation":"Ritsumeikan University"},{"name":"Lanfen Lin","slug":"lanfen-lin","given":"Lanfen","family":"Lin","affiliation":"Zhejiang University"}],"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.","doi":"10.24963/ijcai.2023/144","url":"https://ijcai.org/proceedings/2023/144","pdf":"https://ijcai.org/proceedings/2023/0144.pdf","markdown":"https://ijcai.org/proceedings/2023/144.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/144","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":144,"pages":{"first":1294,"last":1302},"keywords":["Computer Vision: CV: Vision and language","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2019a] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. 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