{"type":"ScholarlyArticle","title":"Diagram Visual Grounding: Learning to See with Gestalt-Perceptual Attention","authors":[{"name":"Xin Hu","slug":"xin-hu","given":"Xin","family":"Hu","affiliation":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering , School of Computer Science and Technology , Xi'an Jiaotong University , China; National Engineering Lab for Big Data Analytics , Xi'an Jiaotong University , China"},{"name":"Lingling Zhang","slug":"lingling-zhang","given":"Lingling","family":"Zhang","affiliation":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering , School of Computer Science and Technology , Xi'an Jiaotong University , China; National Engineering Lab for Big Data Analytics , Xi'an Jiaotong University , China"},{"name":"Jun Liu","slug":"jun-liu","given":"Jun","family":"Liu","affiliation":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering , School of Computer Science and Technology , Xi'an Jiaotong University , China; National Engineering Lab for Big Data Analytics , Xi'an Jiaotong University , China"},{"name":"Xinyu Zhang","slug":"xinyu-zhang","given":"Xinyu","family":"Zhang","affiliation":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering , School of Computer Science and Technology , Xi'an Jiaotong University , China; National Engineering Lab for Big Data Analytics , Xi'an Jiaotong University , China"},{"name":"Wenjun Wu","slug":"wenjun-wu","given":"Wenjun","family":"Wu","affiliation":"Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering , School of Computer Science and Technology , Xi'an Jiaotong University , China; National Engineering Lab for Big Data Analytics , Xi'an Jiaotong University , China"},{"name":"Qianying Wang","slug":"qianying-wang","given":"Qianying","family":"Wang","affiliation":"Lenovo Research , Beijing , China"}],"abstract":"Diagram visual grounding aims to capture the correlation between language expression and local objects in the diagram, and plays an important role in the applications like textbook question answering and cross-modal retrieval. Most diagrams consist of several colors and simple geometries. This results in sparse low-level visual features, which further aggravates the gap between low-level visual and high-level semantic features of diagrams. The phenomenon brings challenges to the diagram visual grounding. To solve the above issues, we propose a gestalt-perceptual attention model to align the diagram objects and language expressions. For low-level visual features, inspired by the gestalt that simulates human visual system, we build a gestalt-perception graph network to make up the features learned by the traditional backbone network. For high-level semantic features, we design a multi-modal context attention mechanism to facilitate the interaction between diagrams and language expressions, so as to enhance the semantics of diagrams. Finally, guided by diagram features and linguistic embedding, the target query is gradually decoded to generate the coordinates of the referred object. By conducting comprehensive experiments on diagrams and natural images, we demonstrate that the proposed model achieves superior performance over the competitors. Our code will be released at https://github.com/AIProCode/GPA.","doi":"10.24963/ijcai.2023/93","url":"https://ijcai.org/proceedings/2023/93","pdf":"https://ijcai.org/proceedings/2023/0093.pdf","markdown":"https://ijcai.org/proceedings/2023/93.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/93","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":93,"pages":{"first":837,"last":845},"keywords":["Computer Vision: CV: Vision and language","Computer Vision: CV: Representation learning","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Carion et al., 2020] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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