{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T07:15:15Z","timestamp":1763968515064,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T00:00:00Z","timestamp":1682985600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T00:00:00Z","timestamp":1682985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-022-04434-y","type":"journal-article","created":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T13:02:32Z","timestamp":1683032552000},"page":"20938-20949","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Searching sharing relationship for instance segmentation decoder"],"prefix":"10.1007","volume":"53","author":[{"given":"Yuling","family":"Xi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,2]]},"reference":[{"key":"4434_CR1","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"4434_CR2","doi-asserted-by":"crossref","unstructured":"Li Y, Qi H, Dai J, Ji X, Wei Y (2017) Fully convolutional instance-aware semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2359\u20132367","DOI":"10.1109\/CVPR.2017.472"},{"key":"4434_CR3","doi-asserted-by":"crossref","unstructured":"Bolya D, Zhou C, Xiao F, Lee YJ (2019) Yolact: Real-time instance segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 9157\u20139166","DOI":"10.1109\/ICCV.2019.00925"},{"key":"4434_CR4","doi-asserted-by":"crossref","unstructured":"Chen H, Sun K, Tian Z, Shen C, Huang Y, Yan Y (2020) BlendMask: Top-down meets bottom-up for instance segmentation. In: Proc. IEEE Conf. computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"4434_CR5","doi-asserted-by":"crossref","unstructured":"Neven D, Brabandere BD, Proesmans M, Gool LV (2019) Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8837\u20138845","DOI":"10.1109\/CVPR.2019.00904"},{"issue":"6","key":"4434_CR6","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence 39(6):1137\u20131149","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"4434_CR7","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"4434_CR8","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"4434_CR9","doi-asserted-by":"crossref","unstructured":"Wang S, Gong Y, Xing J, Huang L, Huang C, Hu W (2020) Rdsnet: a new deep architecture forreciprocal object detection and instance segmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 12208\u201312215","DOI":"10.1609\/aaai.v34i07.6902"},{"key":"4434_CR10","doi-asserted-by":"crossref","unstructured":"Wang Y, Xu Z, Shen H, Cheng B, Yang L (2020) Centermask: single shot instance segmentation with point representation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9313\u20139321","DOI":"10.1109\/CVPR42600.2020.00933"},{"key":"4434_CR11","doi-asserted-by":"crossref","unstructured":"Chen B, Ghiasi G, Liu H, Lin T-Y, Kalenichenko D, Adam H, Le QV (2020) Mnasfpn: Learning latency-aware pyramid architecture for object detection on mobile devices. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13607\u201313616","DOI":"10.1109\/CVPR42600.2020.01362"},{"key":"4434_CR12","unstructured":"Zoph B, Le Q (2017) Neural architecture search with reinforcement learning. In: International conference on learning representations, https:\/\/openreview.net\/forum?id=r1Ue8Hcxg"},{"key":"4434_CR13","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"4434_CR14","unstructured":"Liu H, Simonyan K, Yang Y (2019) Darts: differentiable architecture search. In: 7th International conference on learning representations"},{"key":"4434_CR15","unstructured":"Xu Y, Xie L, Zhang X, Chen X, Qi G, Tian Q, Xiong H (2020) PC-DARTS: partial channel connections for memory-efficient architecture search. In: 8th International conference on learning representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net"},{"key":"4434_CR16","unstructured":"Tan M, Le Q (2019) vEfficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp 6105\u20136114"},{"key":"4434_CR17","doi-asserted-by":"crossref","unstructured":"Ghiasi G, Lin T-Y, Le QV (2019) Nas-fpn: Learning scalable feature pyramid architecture for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7036\u20137045","DOI":"10.1109\/CVPR.2019.00720"},{"key":"4434_CR18","doi-asserted-by":"crossref","unstructured":"Wang N, Gao Y, Chen H, Wang P, Tian Z, Shen C, Zhang Y (2020) Nas-fcos: fast neural architecture search for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.01196"},{"key":"4434_CR19","doi-asserted-by":"crossref","unstructured":"Xu H, Yao L, Zhang W, Liang X, Li Z (2019) Auto-FPN: automatic network architecture adaptation for object detection beyond classification, pp 6649\u20136658","DOI":"10.1109\/ICCV.2019.00675"},{"key":"4434_CR20","doi-asserted-by":"crossref","unstructured":"Li C, Yuan X, Lin C, Guo M, Wu W, Yan J, Ouyang W (2019) Am-lfs: Automl for loss function search. In: Proceedings of the IEEE international conference on computer vision, pp 8410\u20138419","DOI":"10.1109\/ICCV.2019.00850"},{"key":"4434_CR21","first-page":"527","volume":"31","author":"O Sener","year":"2018","unstructured":"Sener O, Koltun V (2018) Multi-task learning as multi-objective optimization. Adv Neural Inf Process Syst 31:527\u2013538","journal-title":"Adv Neural Inf Process Syst"},{"key":"4434_CR22","doi-asserted-by":"crossref","unstructured":"Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"4434_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need, Advances in neural information processing systems, vol 30"},{"key":"4434_CR24","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv:1707.06347"},{"key":"4434_CR25","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"4434_CR26","doi-asserted-by":"crossref","unstructured":"Liu Z, Liew JH, Chen X, Feng J (2021) Dance: a deep attentive contour model for efficient instance segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV), pp 345\u2013354","DOI":"10.1109\/WACV48630.2021.00039"},{"key":"4434_CR27","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H (2020) Conditional convolutions for instance segmentation. In: Proc. Eur. Conf. Computer Vision (ECCV)","DOI":"10.1007\/978-3-030-58452-8_17"},{"key":"4434_CR28","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang R, Shen C, Kong T, Li L (2021) Solo A simple framework for instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2021.3111116"},{"key":"4434_CR29","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2021.07.064","volume":"464","author":"F Jie","year":"2021","unstructured":"Jie F, Nie Q, Li M, Yin M, Jin T (2021) Atrous spatial pyramid convolution for object detection with encoder-decoder. Neurocomputing 464:107\u2013118","journal-title":"Neurocomputing"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04434-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04434-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04434-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T11:08:07Z","timestamp":1695121687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04434-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,2]]},"references-count":29,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4434"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04434-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,5,2]]},"assertion":[{"value":"26 December 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}