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Joint Austrian Computer Vision and Robotics Workshop 2020
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References [1] COCO 2018 Panoptic Segmentation Task. http://cocodataset.org/index.htm#panoptic- leaderboard. Accessed: 2020-01-31. [2] E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, and C. Rother. Dsac- differentiable ransac for camera localization. In Conference on Computer Vision and Pattern Recog- nition, pages6684–6692,2017. [3] L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam. Rethinking Atrous Convolution for Se- mantic Image Segmentation. arXiv:1706.05587, 2017. [4] M.Cordts,M.Omran,S.Ramos,T.Rehfeld,M.En- zweiler, R. Benenson, U. Franke, S. Roth, and B.Schiele. TheCityscapesDataset forSemanticUr- ban Scene Understanding. In Conference on Com- puter Vision and Pattern Recognition, pages 3213– 3223,2016. [5] D. Feng, C. Haase-Schuetz, L. Rosenbaum, H. Hertlein, F. Duffhauss, C. Glaeser, W. Wies- beck, and K. Dietmayer. Deep Multi-Modal Ob- ject Detection and Semantic Segmentation for Au- tonomous Driving: Datasets, Methods, and Chal- lenges. arXiv:1902.07830, 2019. [6] C.-Y. Fu, T. L. Berg, and A. C. Berg. IMP: Instance Mask Projection for High Accuracy Semantic Seg- mentation of Things. In International Conference onComputer Vision, pages 5178–5187, 2019. [7] R. H. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, and S. H. Seung. Digital Selection and Analogue Amplification Coexist in a Cortex- Inspired Silicon Circuit. Nature, 405(6789):947– 951,2000. [8] K.He,G.Gkioxari,P.Dolla´r,andR.Girshick. Mask R-CNN. In International Conference on Computer Vision, pages 2961–2969,2017. [9] K. He, X. Zhang, S. Ren, and J. Sun. Deep Resid- ual Learning for Image Recognition. In Conference on Computer Vision and Pattern Recognition, pages 770–778,2016. [10] S. Ioffe and C. Szegedy. Batch normalization: Ac- celerating deep network training by reducing inter- nal covariate shift. arXiv:1502.03167, 2015. [11] A.Kirillov,R.Girshick,K.He,andP.Dolla´r.Panop- tic Feature Pyramid Networks. In Conference on Computer Vision and Pattern Recognition, pages 6399–6408, 2019. [12] A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dolla´r. Panoptic Segmentation. In Conference on Computer Vision and Pattern Recognition, pages 9404–9413, 2019. [13] A. Kirillov, E. Levinkov, B. Andres, B. Savchyn- skyy, andC.Rother. Instancecut: FromEdges to In- stances with Multicut. In Conference on Computer Vision and Pattern Recognition, pages 5008–5017, 2017. [14] J. Li, A. Raventos, A. Bhargava, T. Tagawa, and A. Gaidon. Learning to Fuse Things and Stuff. arXiv:1812.01192, 2018. [15] Y. Li, X. Chen, Z. Zhu, L. Xie, G. Huang, D. Du, and X. Wang. Attention-Guided Unified Network for Panoptic Segmentation. In Conference on Com- puter Vision and Pattern Recognition, pages 7026– 7035,2019. [16] Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei. Fully Convo- lutional Instance-Aware Semantic Segmentation. In Conference on Computer Vision and Pattern Recog- nition, pages2359–2367, 2017. [17] X. Liang, L. Lin, Y. Wei, X. Shen, J. Yang, and S. Yan. Proposal-Free Network for Instance-Level ObjectSegmentation. IEEETransactionsonPattern Analysis and Machine Intelligence, 40(12):2978– 2991,2017. [18] T.-Y. Lin, P. Dolla´r, R. Girshick, K. He, B. Hari- haran, and S. Belongie. Feature Pyramid Networks for Object Detection. In Conference on Computer Vision and Pattern Recognition, pages 2117–2125, 2017. [19] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Per- ona, D. Ramanan, P. Dolla´r, and C. L. Zitnick. Mi- crosoftCOCO:CommonObjects inContext. InEu- ropeanConferenceonComputerVision, pages740– 755,2014. [20] S. Liu, J. Jia, S. Fidler, and R. Urtasun. SGN: Se- quentialGroupingNetworksfor InstanceSegmenta- tion. In International Conference on Computer Vi- sion, pages3496–3504,2017. [21] S.Liu,L.Qi,H.Qin,J.Shi,andJ.Jia.PathAggrega- tion Network for Instance Segmentation. In Confer- ence on Computer Vision and Pattern Recognition, pages 8759–8768,2018. [22] J. Long, E. Shelhamer, and T. Darrell. Fully Con- volutional Networks for Semantic Segmentation. In Conference on Computer Vision and Pattern Recog- nition, pages3431–3440, 2015. [23] A. Petrovai and S. Nedevschi. Multi-Task Network for Panoptic Segmentation in Automated Driving. In Intelligent Transportation Systems Conference, pages 2394–2401,2019. [24] O. Ronneberger, P. Fischer, and T. Brox. U- Net: Convolutional Networks for Biomedical Im- age Segmentation. In Medical Image Computing and Computer-Assisted Intervention, pages 234– 241,2015. [25] J. Tighe, M. Niethammer, and S. Lazebnik. Scene Parsing with Object Instances and Occlusion Order- ing. In Conference on Computer Vision and Pattern Recognition, pages 3748–3755,2014. 77
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Joint Austrian Computer Vision and Robotics Workshop 2020
Titel
Joint Austrian Computer Vision and Robotics Workshop 2020
Herausgeber
Graz University of Technology
Ort
Graz
Datum
2020
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-85125-752-6
Abmessungen
21.0 x 29.7 cm
Seiten
188
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Joint Austrian Computer Vision and Robotics Workshop 2020