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Die automatischen Untertitel, die mit Whisper Open AI in diesem Video-Player (und im Multistream-Video-Player) generiert werden, dienen der Bequemlichkeit und Barrierefreiheit. Es ist jedoch zu beachten, dass die Genauigkeit und Interpretation variieren können. Für mehr Informationen lesen Sie bitte die FAQs (Absatz 14)
Aufnahme Datum 2020-10-12

Kurs-Verknüpfung

Deep Learning - Plain Version

Zugang

Frei

Sprache

Englisch

Einrichtung

Lehrstuhl für Informatik 5 (Mustererkennung)

Produzent

Lehrstuhl für Informatik 5 (Mustererkennung)

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.

Nächstes Video

Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei

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