{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T10:14:57Z","timestamp":1725790497981},"reference-count":42,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"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":[[2021,10]]},"DOI":"10.1109\/iccv48922.2021.00432","type":"proceedings-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T22:08:02Z","timestamp":1646086082000},"source":"Crossref","is-referenced-by-count":6,"title":["Deep Structured Instance Graph for Distilling Object Detectors"],"prefix":"10.1109","author":[{"given":"Yixin","family":"Chen","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Pengguang","family":"Chen","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Shu","family":"Liu","sequence":"additional","affiliation":[{"name":"SmartMore"}]},{"given":"Liwei","family":"Wang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Jiaya","family":"Jia","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"zagoruyko","year":"2016","journal-title":"ICLRE"},{"key":"ref38","article-title":"Detectron2","author":"wu","year":"2019"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref32","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"ICML"},{"key":"ref31","article-title":"Distilling object detectors with task adaptive regularization","author":"sun","year":"2020","journal-title":"CoRR"},{"key":"ref30","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"ICLRE"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00507"},{"key":"ref34","article-title":"Visualizing data using t-SNE","author":"der maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref40","article-title":"Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors","author":"zhang","year":"2021","journal-title":"ICLRE"},{"key":"ref11","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"ArXiv"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00378"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref14","article-title":"Knowledge distillation by on-the-fly native ensemble","author":"lan","year":"2018","journal-title":"NIPS"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.776"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00292"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref18","article-title":"Microsoft COCO: common objects in context","author":"lin","year":"2014","journal-title":"ECCV"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref4","article-title":"FastR-CNN","author":"girshick","year":"2015","journal-title":"ICCV"},{"key":"ref27","article-title":"Fitnets: Hints for thin deep nets","author":"romero","year":"2015","journal-title":"ICLRE"},{"key":"ref3","article-title":"R-FCN: object detection via region-based fully convolutional networks","author":"dai","year":"2016","journal-title":"NIPS"},{"key":"ref6","article-title":"Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"ICLRE"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref7","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"NIPS"},{"key":"ref2","article-title":"Learning efficient object detection models with knowledge distillation","author":"chen","year":"2017","journal-title":"NIPS"},{"key":"ref9","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","author":"he","year":"2014","journal-title":"ECCV"},{"key":"ref1","article-title":"Cascade r-cnn: High quality object detection and instance segmentation","author":"cai","year":"2019","journal-title":"TPAMI"},{"key":"ref20","article-title":"SSD: single shot multibox detector","author":"liu","year":"2016","journal-title":"ECCV"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00730"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00271"},{"key":"ref42","article-title":"Dorefa-net: Training low bitwidth convo¬lutional neural networks with low bitwidth gradients","author":"zhou","year":"2016","journal-title":"CoRR"},{"key":"ref24","article-title":"Xnor-net: Imagenet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"ECCV"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00454"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref26","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"NIPS"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"}],"event":{"name":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Montreal, QC, Canada","start":{"date-parts":[[2021,10,10]]},"end":{"date-parts":[[2021,10,17]]}},"container-title":["2021 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9709627\/9709628\/09711100.pdf?arnumber=9711100","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T19:45:34Z","timestamp":1657827934000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9711100\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/iccv48922.2021.00432","relation":{},"subject":[],"published":{"date-parts":[[2021,10]]}}}