{"type":"ScholarlyArticle","title":"MISA: MIning Saliency-Aware Semantic Prior for Box Supervised Instance Segmentation","authors":[{"name":"Hao Zhu","slug":"hao-zhu","given":"Hao","family":"Zhu","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Yan Zhu","slug":"yan-zhu","given":"Yan","family":"Zhu","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Jiayu Xiao","slug":"jiayu-xiao","given":"Jiayu","family":"Xiao","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Yike Ma","slug":"yike-ma","given":"Yike","family":"Ma","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China"},{"name":"Yucheng Zhang","slug":"yucheng-zhang","given":"Yucheng","family":"Zhang","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China"},{"name":"Jintao Li","slug":"jintao-li","given":"Jintao","family":"Li","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China"},{"name":"Feng Dai","slug":"feng-dai","given":"Feng","family":"Dai","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China"}],"abstract":"Box supervised instance segmentation (BSIS) aims to achieve an effective trade-off between annotation costs and model performance by solely relying on bounding box annotations during training process. However, we observe that BSIS model is bottlenecked by the intricate objective under limited guidance, and tends to sacrifice segmentation capability in order to effectively recognize multiple instances. To boost the BSIS model's perceptual ability for object shape and contour, we introduce MISA, that is, MIning Saliency-Aware semantic prior from a well-optimized box supervised semantic segmentation (BSSS) network, and incorporating cross-model guidance into the learning process of BSIS. Specifically, we first design a Frequency-Space Distillation (FSD) module to extract assorted salient prior knowledge from BSSS model, and perform cross-model alignment for transfering the prior to BSIS model. Furthermore, we introduce Semantic-Enhanced Pairwise Affinity (SEPA), which borrows the object perceptual ability of BSSS model to emphasize the contribution of salient objects for pairwise affinity, providing more accurate guidance for the BSIS network. Extensive experiments show that our proposed MISA consistently surpasses the existing state-of-the-art methods by a large margin in the BSIS scenario.","doi":"10.24963/ijcai.2024/199","url":"https://ijcai.org/proceedings/2024/199","pdf":"https://ijcai.org/proceedings/2024/0199.pdf","markdown":"https://ijcai.org/proceedings/2024/199.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/199","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":199,"pages":{"first":1798,"last":1806},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Araslanov and Roth, 2020] Nikita Araslanov and Stefan Roth. 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