{"type":"ScholarlyArticle","title":"A Dual Semantic-Aware Recurrent Global-Adaptive Network for Vision-and-Language Navigation","authors":[{"name":"Liuyi Wang","slug":"liuyi-wang","given":"Liuyi","family":"Wang","affiliation":"Tongji University , Shanghai , China"},{"name":"Zongtao He","slug":"zongtao-he","given":"Zongtao","family":"He","affiliation":"Tongji University , Shanghai , China"},{"name":"Jiagui Tang","slug":"jiagui-tang","given":"Jiagui","family":"Tang","affiliation":"Tongji University , Shanghai , China"},{"name":"Ronghao Dang","slug":"ronghao-dang","given":"Ronghao","family":"Dang","affiliation":"Tongji University , Shanghai , China"},{"name":"Naijia Wang","slug":"naijia-wang","given":"Naijia","family":"Wang","affiliation":"Tongji University , Shanghai , China"},{"name":"Chengju Liu","slug":"chengju-liu","given":"Chengju","family":"Liu","affiliation":"Tongji University , Shanghai , China; Tongji Artificial Intelligence (Suzhou) Research Institute , Suzhou , China {wly , xingchen327 , 2130701 ,"},{"name":"Qijun Chen","slug":"qijun-chen","given":"Qijun","family":"Chen","affiliation":"Tongji University , Shanghai , China"}],"abstract":"Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. First, DSRG proposes an instruction-guidance linguistic module (IGL) and an appearance-semantics visual module (ASV) for boosting vision and language semantic learning respectively. For the memory mechanism, a global adaptive aggregation module (GAA) is devised for explicit panoramic observation fusion, and a recurrent memory fusion module (RMF) is introduced to supply implicit temporal hidden states. Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. Code is available at https://github.com/CrystalSixone/DSRG.","doi":"10.24963/ijcai.2023/164","url":"https://ijcai.org/proceedings/2023/164","pdf":"https://ijcai.org/proceedings/2023/0164.pdf","markdown":"https://ijcai.org/proceedings/2023/164.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/164","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":164,"pages":{"first":1479,"last":1487},"keywords":["Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Vision and language","Computer Vision: CV: Structural and model-based approaches, knowledge representation and reasoning"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2021] Dong An, Yuankai Qi, Yan Huang, Qi Wu, Liang Wang, and Tieniu Tan. 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