{"type":"ScholarlyArticle","title":"Projection, Interaction and Fusion: A Progressive Difference Fusion Network for Salient Object Detection","authors":[{"name":"Xiao Ke","slug":"xiao-ke","given":"Xiao","family":"Ke","affiliation":""},{"name":"Weijie Zhou","slug":"weijie-zhou","given":"Weijie","family":"Zhou","affiliation":""},{"name":"Yuzhen Niu","slug":"yuzhen-niu","given":"Yuzhen","family":"Niu","affiliation":""}],"abstract":"In recent years, deep learning-based Salient Object Detection (SOD) methods have made tremendous progress; however, their performance in complex scenarios has reached a bottleneck. In this paper, we propose a novel Progressive Difference Fusion Network (PDFNet) based on fine-grained feature fusion. First, to address the scale variability of salient objects, we introduce a Self-Guided Module (SGM) with dynamic receptive fields. Second, to tackle the shape variability of salient objects, we design a Feature Aggregation Module (FAM) incorporating cross convolutions and a feedback loop. Finally, to alleviate the issue of confusion between global and detail information during multi-scale feature fusion in existing models, we develop a Progressive Difference Fusion Unit (PDFU) to project multi-scale features into fine-grained nodes and enhance them through node interaction based on difference features. Additionally, we propose a Conditional Random Field Based on Patch (CRFbp), which focuses on handling discrete points, further improving the model’s performance. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) performance on five benchmark datasets. Code is available at: https://github.com/pdfnet2025/PDFNet.git.","doi":"10.24963/ijcai.2025/145","url":"https://ijcai.org/proceedings/2025/145","pdf":"https://ijcai.org/proceedings/2025/0145.pdf","markdown":"https://ijcai.org/proceedings/2025/145.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/145","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":145,"pages":{"first":1296,"last":1304},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}