{"type":"ScholarlyArticle","title":"H^2Net: Homo- and Heterogeneous Networks for Unified Segmentation","authors":[{"name":"Jinyu Han","slug":"jinyu-han","given":"Jinyu","family":"Han","affiliation":""},{"name":"Changguang Wu","slug":"changguang-wu","given":"Changguang","family":"Wu","affiliation":""},{"name":"Fuming Sun","slug":"fuming-sun","given":"Fuming","family":"Sun","affiliation":""},{"name":"Mengyin Wang","slug":"mengyin-wang","given":"Mengyin","family":"Wang","affiliation":""},{"name":"Jinhui Tang","slug":"jinhui-tang","given":"Jinhui","family":"Tang","affiliation":""}],"abstract":"Unified segmentation aims to consolidate multiple vision tasks into a single model, yet faces two core challenges: learning robust homogeneous features (e.g., shared low- and mid-level cues) to enable cross-domain knowledge transfer, while disentangling heterogeneous features (e.g., task-specific semantic objectives) to avoid negative transfer and preserve task independence.\r\nTo address these challenges, we propose the Homo- and Heterogeneous Network (H2Net), a unified framework that jointly models shared homogeneous representations and task-specific heterogeneous features. Specifically, H2Net incorporates a Cross-Modal Structure Enhancement Module (CSEM), which integrates auxiliary depth priors via joint frequency–spatial cross-modal attention to strengthen task-agnostic structural representations. In addition, a Task Adapter Pool (TAP) is introduced to model task-specific heterogeneous features by assigning dedicated adapters to individual tasks, enabling task-aware feature modulation and semantic disentanglement within a shared backbone. Extensive experiments on benchmarks spanning eight tasks demonstrate the effectiveness of the proposed approach and its superior performance. Code and results will be available at https://h2net-ijcai26.github.io.","doi":"10.24963/ijcai.2026/127","url":"https://ijcai.org/proceedings/2026/127","pdf":"https://ijcai.org/proceedings/2026/0127.pdf","markdown":"https://ijcai.org/proceedings/2026/127.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/127","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":127,"pages":{"first":1133,"last":1142},"keywords":["Computer Vision: Biomedical image analysis","Computer Vision: Segmentation, grouping and shape analysis","Computer Vision: Multimodal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}