{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:25:19Z","timestamp":1778171119367,"version":"3.51.4"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1109\/icra40945.2020.9196905","type":"proceedings-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T21:25:46Z","timestamp":1600205146000},"page":"8433-8439","source":"Crossref","is-referenced-by-count":24,"title":["AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery"],"prefix":"10.1109","author":[{"given":"Mobarakol","family":"Islam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. S.","family":"Vibashan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2750080"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref31","article-title":"Pcas: Pruning channels with attention statistics for deep network compression","author":"yamamoto","year":"2018"},{"key":"ref30","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2016"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref36","first-page":"325","article-title":"Bisenet: Bilateral segmentation network for real-time semantic segmentation","author":"yu","year":"2018","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"ref35","article-title":"Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation","author":"iglovikov","year":"2018"},{"key":"ref34","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8206462"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2900854"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"ref13","first-page":"91","article-title":"Faster r-cnn: Towards realtime object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2665671"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CAC.2017.8243236"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/24699322.2017.1378777"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2917163"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_75"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref28","article-title":"Skip connections eliminate singularities","author":"orhan","year":"2017"},{"key":"ref4","article-title":"Yolov3: An incremental improvement","author":"redmon","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2017.00012"},{"key":"ref3","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32254-0_46"},{"key":"ref29","article-title":"A survey on multi-task learning","author":"zhang","year":"2017"},{"key":"ref5","article-title":"Visualizing uncertainty and saliency maps of deep convolutional neural networks for medical imaging applications","author":"seo","year":"2019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref7","first-page":"21","article-title":"Ssd: Single shot multibox detector","author":"liu","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref2","article-title":"Deep residual learning for instrument segmentation in robotic surgery","author":"pakhomov","year":"2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00100"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2147\/RSRR.S119317"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref22","article-title":"Real-time joint object detection and semantic segmentation network for automated driving","author":"sistu","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.447"},{"key":"ref24","article-title":"2017 robotic instrument segmentation challenge","author":"allan","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.579"},{"key":"ref26","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"hu","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref25","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"}],"event":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","location":"Paris, France","start":{"date-parts":[[2020,5,31]]},"end":{"date-parts":[[2020,8,31]]}},"container-title":["2020 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9187508\/9196508\/09196905.pdf?arnumber=9196905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:27:24Z","timestamp":1656376044000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9196905\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icra40945.2020.9196905","relation":{},"subject":[],"published":{"date-parts":[[2020,5]]}}}