{"type":"ScholarlyArticle","title":"Syntactic Structure-Guided Visual Grounding with Subject-Centric Feature Enhancement and Verification","authors":[{"name":"Jiepeng Cai","slug":"jiepeng-cai","given":"Jiepeng","family":"Cai","affiliation":""},{"name":"Zhen Xu","slug":"zhen-xu","given":"Zhen","family":"Xu","affiliation":""},{"name":"Tiesong Zhao","slug":"tiesong-zhao","given":"Tiesong","family":"Zhao","affiliation":""},{"name":"Hau-San Wong","slug":"hau-san-wong","given":"Hau San","family":"Wong","affiliation":""},{"name":"Si Wu","slug":"si-wu","given":"Si","family":"Wu","affiliation":""}],"abstract":"Visual grounding aims to localize target objects based on natural language descriptions, and the core challenge lies in the cross-modal gap, which is partly caused by the significant differences in semantic structure between language and vision. Existing methods typically rely on holistic sentence-level semantic representations to modulate visual features, while overlooking the inherent structure of textual prompts. In this work, we propose a Syntactic Structure-guided Visual Grounding framework, referred to as SSVG. Specifically, to inject syntactic priors into unstructured visual representations, we design a semantic structure-based feature refinement module to adaptively modulate subject-centric and contextual visual features. To perform cross-modal alignment, we further incorporate a visual semantic consistency verification module, which leverages a subject-aware contrastive learning strategy to constrain and verify the semantic correspondence between the visual prediction and subject-level textual representation, thereby enhancing the model's robustness against semantically similar distractors. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art methods, and detailed analyses verify the effectiveness of each component.","doi":"10.24963/ijcai.2026/104","url":"https://ijcai.org/proceedings/2026/104","pdf":"https://ijcai.org/proceedings/2026/0104.pdf","markdown":"https://ijcai.org/proceedings/2026/104.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/104","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":104,"pages":{"first":926,"last":934},"keywords":["Computer Vision: Segmentation, grouping and shape analysis","Computer Vision: Multimodal learning","Computer Vision: Recognition (object detection, categorization)"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}