{"type":"ScholarlyArticle","title":"SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos","authors":[{"name":"Wenxuan Liu","slug":"wenxuan-liu","given":"Wenxuan","family":"Liu","affiliation":""},{"name":"Yao Deng","slug":"yao-deng","given":"Yao","family":"Deng","affiliation":""},{"name":"Kang Chen","slug":"kang-chen","given":"Kang","family":"Chen","affiliation":""},{"name":"Xian Zhong","slug":"xian-zhong","given":"Xian","family":"Zhong","affiliation":""},{"name":"Zhaofei Yu","slug":"zhaofei-yu","given":"Zhaofei","family":"Yu","affiliation":""},{"name":"Tiejun Huang","slug":"tiejun-huang","given":"Tiejun","family":"Huang","affiliation":""}],"abstract":"Existing saliency detection methods struggle in real-world scenarios due to motion blur and occlusions. In contrast, spike cameras, with their high temporal resolution, significantly enhance visual saliency maps. However, the composite noise inherent to spike camera imaging introduces discontinuities in saliency detection. Low-quality samples further distort model predictions, leading to saliency bias. To address these challenges, we propose Spike-navigated Optimal TrAnsport Saliency Region Detection (SOTA), a framework that leverages the strengths of spike cameras while mitigating biases in both spatial and temporal dimensions. Our method introduces Spike-based Micro-debias (SM) to capture subtle frame-to-frame variations and preserve critical details, even under minimal scene or lighting changes. Additionally, Spike-based Global-debias (SG) refines predictions by reducing inconsistencies across diverse conditions. Extensive experiments on real and synthetic datasets demonstrate that SOTA outperforms existing methods by eliminating composite noise bias. Our code and dataset will be released at https://github.com/lwxfight/sota.","doi":"10.24963/ijcai.2025/181","url":"https://ijcai.org/proceedings/2025/181","pdf":"https://ijcai.org/proceedings/2025/0181.pdf","markdown":"https://ijcai.org/proceedings/2025/181.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/181","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":181,"pages":{"first":1621,"last":1629},"keywords":["Computer Vision: CV: Multimodal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}