{"type":"ScholarlyArticle","title":"Dynamic Multi-Path Retrieval for Knowledge-based Visual Question Answering","authors":[{"name":"Zeyu Song","slug":"zeyu-song","given":"Zeyu","family":"Song","affiliation":""},{"name":"Yimin Deng","slug":"yimin-deng","given":"Yimin","family":"Deng","affiliation":""},{"name":"Yuxin Zhang","slug":"yuxin-zhang","given":"Yuxin","family":"Zhang","affiliation":""},{"name":"Guoshuai Zhao","slug":"guoshuai-zhao","given":"Guoshuai","family":"Zhao","affiliation":""},{"name":"Chengxu Liu","slug":"chengxu-liu","given":"Chengxu","family":"Liu","affiliation":""},{"name":"Jialie Shen","slug":"jialie-shen","given":"Jialie","family":"Shen","affiliation":""},{"name":"Xueming Qian","slug":"xueming-qian","given":"Xueming","family":"Qian","affiliation":""}],"abstract":"Knowledge-based Visual Question Answering (KB-VQA) requires models to answer visual questions by reasoning over external knowledge beyond the given image. Existing approaches suffer from two main limitations. First, candidate knowledge is often retrieved in a single modality, either textual or visual, which prevents effective use of heterogeneous and complementary evidence. Second, current approaches typically employ fixed fusion weights, ignoring the varying importance of modalities for different queries. Consequently, irrelevant evidence is introduced while critical   knowledge may be overlooked. To address these issues, we propose Dynamic Multi-Path Retrieval for KB-VQA (DMRAG). Our framework retrieves candidates through multiple retrieval paths that capture complementary visual and semantic cues. It then performs Question-Adaptive Gated Fusion (QGF) to balance contributions from different modalities according to the query’s information need. The fused candidates are further refined via multimodal rearrangement to support accurate answer generation. Experiments on the E-VQA and InfoSeek datasets show that DMRAG improves both retrieval recall and answer accuracy over prior methods, demonstrating its effectiveness for KB-VQA. Our code is available at https://github.com/qwqq335/DMRAG.","doi":"10.24963/ijcai.2026/182","url":"https://ijcai.org/proceedings/2026/182","pdf":"https://ijcai.org/proceedings/2026/0182.pdf","markdown":"https://ijcai.org/proceedings/2026/182.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/182","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":182,"pages":{"first":1631,"last":1639},"keywords":["Computer Vision: Vision, language and reasoning","Computer Vision: Image and video retrieval","Computer Vision: Multimodal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}