Identifying Style of 3D Shapes using Deep Metric Learning

Computer Graphics Forum

Volumn 35, Issue 5, 2016, Pages 207-215

  Lim, Isaak ^a   Gehre, Anne ^a   Kobbelt, Leif ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

deep metric learning; style similarity

Indexed keywords

COMPUTER GRAPHICS;

CLASSIFICATION ACCURACY; DIFFERENT STRUCTURE; HETEROGENEOUS DATA SOURCES; HIGH-LEVEL FEATURES; METRIC LEARNING; SIMILARITY METRICS; STATE OF THE ART; STYLE SIMILARITY;

CLASSIFICATION (OF INFORMATION);

EID: 84982160994     PISSN: 01677055     EISSN: 14678659     Source Type: Journal    
DOI: 10.1111/cgf.12977     Document Type: Article

References (24)

1. Bell S., Bala K.: Learning visual similarity for product design with convolutional neural networks. ACM Trans. Graph. 34, 4 (July 2015), 98:1–98:10. 3, 5
2. Clevert D.-A., Unterthiner T., Hochreiter S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015). 5
3. Cui Y., Zhou F., Lin Y., Belongie S.J.: Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. CoRR abs/1512.05227 (2015). 2
4. Dieleman S., SchlÃijter J., Raffel C., Olson E., SÃÿnderby S. K., Nouri D., Maturana D., Thoma M., Battenberg E., Kelly J., Fauw J.D., Heilman M., diogo149, McFee B., Weideman H., takacsg84, peterderivaz, Jon, instagibbs, Rasul D.K., CongLiu, Britefury, Degrave J.: Lasagne: First release., Aug. 2015. 5
5. Hoffer E., Ailon N.: Deep metric learning using triplet network. In Similarity-Based Pattern Recognition. Springer, 2015, pp. 84–92. 2, 4
6. Hadsell R., Chopra S., LeCun Y.: Dimensionality reduction by learning an invariant mapping. In Computer vision and pattern recognition, 2006 IEEE computer society conference on (2006), vol. 2, IEEE, pp. 1735–1742. 4
7. Hu J., Lu J., Tan Y.-P.: Discriminative deep metric learning for face verification in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1875–1882. 3
8. Ioffe S., Szegedy C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015). 5
9. Krizhevsky A., Sutskever I., Hinton G.E.: Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (2012), pp. 1097–1105. 3, 5
10. Liu T., Hertzmann A., Li W., Funkhouser T.: Style compatibility for 3d furniture models. ACM Transactions on Graphics (TOG) 34, 4 (2015), 85. 2
11. Lun Z., Kalogerakis E., Sheffer A.: Elements of style: learning perceptual shape style similarity. ACM Transactions on Graphics (TOG) 34, 4 (2015), 84. 2, 3, 6, 7
12. Li Y., Su H., Qi C.R., Fish N., Cohen-Or D., Guibas L.J.: Joint embeddings of shapes and images via cnn image purification. ACM Transactions on Graphics (TOG) 34, 6 (2015), 234. 3
13. Möbius J., Kobbelt L.: Openflipper: An open source geometry processing and rendering framework. In Curves and Surfaces, Boissonnat J.-D., Chenin P., Cohen A., Gout C., Lyche T., Mazure M.-L., Schumaker L., (Eds.), vol. 6920 of Lecture Notes in Computer Science. Springer Berlin/ Heidelberg, 2012, pp. 488–500. 8
14. Pixabay. https://pixabay.com/. Accessed: 2016-04-01 Published under Creative Commons CC0. 1
15. Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A.C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115, 3 (2015), 211–252. 5
16. Sun Y., Chen Y., Wang X., Tang X.: Deep learning face representation by joint identification-verification. In Advances in Neural Information Processing Systems (2014), pp. 1988–1996. 3
17. Srivastava N., Hinton g., Krizhevsky A., Sutskever I., Salakhutdinov R.: Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929–1958. 5
18. Schroff F., Kalenichenko D., Philbin J.: Facenet: A unified embedding for face recognition and clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015). 3
19. Su H., Maji S., Kalogerakis E., Learned-Miller E.: Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 945–953. 3
20. Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014). 5
21. Taigman Y., Yang M., Ranzato M., Wolf L.: Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1701–1708. 3
22. Wohlhart P., Lepetit V.: Learning descriptors for object recognition and 3d pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3109–3118. 3
23. Wang J., Song Y., Leung T., Rosenberg C., Wang J., Philbin J., Chen B., Wu Y.: Learning fine-grained image similarity with deep ranking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1386–1393. 3, 4
24. Zeiler M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012). 5, 7