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Recording date 2020-10-12

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Informatik 5 (Mustererkennung)

Producer

Lehrstuhl für Informatik 5 (Mustererkennung)

Format

lecture

Deep Learning - Segmentation and Object Detection Part 5

In this video, we look at instance segmentation and introduce the concepts of Mask-RCNN.

For reminders to watch the new video follow on Twitter or LinkedIn.

Additional References
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
Retina-net Figure by Marc Aubreville
DarkNet Library
Joseph Redmond CV

Further Reading:
A gentle Introduction to Deep Learning

References
[1] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. “Segnet: A deep convolutional encoder-decoder architecture for image segmentation”. In: arXiv preprint arXiv:1511.00561 (2015). arXiv: 1311.2524.
[2] Xiao Bian, Ser Nam Lim, and Ning Zhou. “Multiscale fully convolutional network with application to industrial inspection”. In: Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on. IEEE. 2016, pp. 1–8.
[3] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, et al. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs”. In: CoRR abs/1412.7062 (2014). arXiv: 1412.7062.
[4] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, et al. “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs”. In: arXiv preprint arXiv:1606.00915 (2016).
[5] S. Ren, K. He, R. Girshick, et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”. In: vol. 39. 6. June 2017, pp. 1137–1149.
[6] R. Girshick. “Fast R-CNN”. In: 2015 IEEE International Conference on Computer Vision (ICCV). Dec. 2015, pp. 1440–1448.
[7] Tsung-Yi Lin, Priya Goyal, Ross Girshick, et al. “Focal loss for dense object detection”. In: arXiv preprint arXiv:1708.02002 (2017).
[8] Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, et al. “A Review on Deep Learning Techniques Applied to Semantic Segmentation”. In: arXiv preprint arXiv:1704.06857 (2017).
[9] Bharath Hariharan, Pablo Arbeláez, Ross Girshick, et al. “Simultaneous detection and segmentation”. In: European Conference on Computer Vision. Springer. 2014, pp. 297–312.
[10] Kaiming He, Georgia Gkioxari, Piotr Dollár, et al. “Mask R-CNN”. In: CoRR abs/1703.06870 (2017). arXiv: 1703.06870.
[11] N. Dalal and B. Triggs. “Histograms of oriented gradients for human detection”. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 1. June 2005, 886–893 vol. 1.
[12] Jonathan Huang, Vivek Rathod, Chen Sun, et al. “Speed/accuracy trade-offs for modern convolutional object detectors”. In: CoRR abs/1611.10012 (2016). arXiv: 1611.10012.
[13] Jonathan Long, Evan Shelhamer, and Trevor Darrell. “Fully convolutional networks for semantic segmentation”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, pp. 3431–3440.
[14] Pauline Luc, Camille Couprie, Soumith Chintala, et al. “Semantic segmentation using adversarial networks”. In: arXiv preprint arXiv:1611.08408 (2016).
[15] Christian Szegedy, Scott E. Reed, Dumitru Erhan, et al. “Scalable, High-Quality Object Detection”. In: CoRR abs/1412.1441 (2014). arXiv: 1412.1441.
[16] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. “Learning deconvolution network for semantic segmentation”. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, pp. 1520–1528.
[17] Adam Paszke, Abhishek Chaurasia, Sangpil Kim, et al. “Enet: A deep neural network architecture for real-time semantic segmentation”. In: arXiv preprint arXiv:1606.02147 (2016).
[18] Pedro O Pinheiro, Ronan Collobert, and Piotr Dollár. “Learning to segment object candidates”. In: Advances in Neural Information Processing Systems. 2015, pp. 1990–1998.
[19] Pedro O Pinheiro, Tsung-Yi Lin, Ronan Collobert, et al. “Learning to refine object segments”. In: European Conference on Computer Vision. Springer. 2016, pp. 75–91.
[20] Ross B. Girshick, Jeff Donahue, Trevor Darrell, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation”. In: CoRR abs/1311.2524 (2013). arXiv: 1311.2524.
[21] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. “U-net: Convolutional networks for biomedical image segmentation”. In: MICCAI. Springer. 2015, pp. 234–241.
[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, et al. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition”. In: Computer Vision – ECCV 2014. Cham: Springer International Publishing, 2014, pp. 346–361.
[23] J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, et al. “Selective Search for Object Recognition”. In: International Journal of Computer Vision 104.2 (Sept. 2013), pp. 154–171.
[24] Wei Liu, Dragomir Anguelov, Dumitru Erhan, et al. “SSD: Single Shot MultiBox Detector”. In: Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016, pp. 21–37.
[25] P. Viola and M. Jones. “Rapid object detection using a boosted cascade of simple features”. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision Vol. 1. 2001, pp. 511–518.
[26] J. Redmon, S. Divvala, R. Girshick, et al. “You Only Look Once: Unified, Real-Time Object Detection”. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2016, pp. 779–788.
[27] Joseph Redmon and Ali Farhadi. “YOLO9000: Better, Faster, Stronger”. In: CoRR abs/1612.08242 (2016). arXiv: 1612.08242.
[28] Fisher Yu and Vladlen Koltun. “Multi-scale context aggregation by dilated convolutions”. In: arXiv preprint arXiv:1511.07122 (2015).
[29] Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, et al. “Conditional Random Fields as Recurrent Neural Networks”. In: CoRR abs/1502.03240 (2015). arXiv: 1502.03240.
[30] Alejandro Newell, Kaiyu Yang, and Jia Deng. “Stacked hourglass networks for human pose estimation”. In: European conference on computer vision. Springer. 2016, pp. 483–499.

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