Fabian Junkert
- Mathieu Aubry, Daniel Maturana, Alexei Efros, Bryan Russell, and Josef Sivic. 2014. Seeing 3D chairs: Exemplar Part-based 2D-3D Alignment using a Large Dataset of CAD models. In Proc. CVPR. 3762--3769. Google Scholar
Digital Library
- Mathieu Aubry and Bryan C. Russell. 2015. Understanding Deep Features With Computer-Generated Imagery. In Proc. ICCV. 2875--2883. Google ScholarDigital Library
- Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, and carsten Rother. 2016. Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image. In Proc. CVPR.Google ScholarCross Ref
- Menglei Chai, Tianjia Shao, Hongzhi Wu, Yanlin Weng, and Kun Zhou. 2016. AutoHair: Fully Automatic Hair Modeling from a Single Image. ACM Trans. Graph. 35, 4, Article 116 (2016), 12 pages. Google ScholarDigital Library
- Navneet Dalal and Bill Triggs. 2005. Histograms of Oriented Gradients for Human Detection. In Proc. CVPR. 886--893. Google ScholarDigital Library
- Mathias Eitz, Ronald Richter, Tamy Boubekeur, Kristian Hildebrand, and Marc Alexa. 2012. Sketch-Based Shape Retrieval. ACM Trans. Graph. (Proc. SIGGRAPH) 31, 4 (2012), 31:1--31:10. Google ScholarDigital Library
- Jan C. Gemert, Jan-Mark Geusebroek, Cor J. Veenman, and Arnold W. Smeulders. 2008. Kernel Codebooks for Scene Categorization. In Proc. ECCV. 696--709. Google ScholarDigital Library
- Johann August Grunert. 1841. Das Pothenot'sche Problem, in erweiterter Gestalt; nebst Bemerkungen über seine Anwendungen in der Geodäsie. Grunerts Archiv für Mathematik und Physik 1 (1841), 238--248.Google Scholar
- Joel A. Hesch and Stergios I. Roumeliotis. 2011. A Direct Least-Squares (DLS) method for PnP.. In Proc. ICCV, D. N. Metaxas and et al. (Eds.). 383--390. Google ScholarDigital Library
- Chaoqun Hong, Jun Yu, Jian Wan, Dacheng Tao, and Meng Wang. 2015. Multi-modal Deep Autoencoder for Human Pose Recovery. IEEE Trans. Image Processing 24, 12 (2015).Google Scholar
- Moos Hueting, Maks Ovsjanikov, and Niloy J. Mitra. 2015. CrossLink: Joint Understanding of Image and 3D Model Collections Through Shape and Camera Pose Variations. ACM TOG 34, 6 (2015), 233:1--233:13. Google ScholarDigital Library
- Mi-Young Huh, Pulkit Agrawal, and Alexei A. Efros. 2016. What makes ImageNet good for transfer learning? CoRR abs/1608.08614 (2016). http://arxiv.org/abs/ 1608.08614Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980Google Scholar
- Yangyan Li, Hao Su, Charles Ruizhongtai Qi, Noa Fish, Daniel Cohen-Or, and Leonidas J. Guibas. 2015. Joint Embeddings of Shapes and Images via CNN Image Purification. ACM TOG, Article 234 (2015), 12 pages. Google ScholarDigital Library
- J. J. Lim, H. Pirsiavash, and A. Torralba. 2013. Parsing IKEA Objects: Fine Pose Estimation. In Proc. ICCV . 2992--2999. Google ScholarDigital Library
- Mingsheng Long and Jianmin Wang. 2015. Learning Transferable Features with Deep Adaptation Networks. CoRR abs/1502.02791 (2015). http://arxiv.org/abs/ 1502.02791Google Scholar
- Francisco Massa, Bryan C. Russell, and Mathieu Aubry. 2016. Deep Exemplar 2D-3D Detection by Adapting From Real to Rendered Views. In Proc. CVPR.Google ScholarCross Ref
- Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko. 2014. Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images. CoRR abs/1412.7122 (2014).Google Scholar
- Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko. 2015. Learning Deep Object Detectors From 3D Models. In Proc. ICCV. 1278--1286. Google ScholarDigital Library
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexan- der C. Berg, and Li Fei-Fei. 2015. ImageNet Large Scale Visual Recognition Challenge. Proc. IJCV 115, 3 (2015), 211--252. Google ScholarDigital Library
- Torsten Sattler, Bastian Leibe, and Leif Kobbelt. 2011. Fast Image-Based Localiza- tion using Direct 2D-to-3D Matching. In Proc. ICCV. 667--674. Google ScholarDigital Library
- Alireza Shafaei, James J. Little, and Mark Schmidt. 2016. Play and Learn: Using Video Games to Train Computer Vision Models. CoRR abs/1608.01745 (2016). http://arxiv.org/abs/1608.01745Google Scholar
- Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik G. Learned-Miller. 2015. Multi-view convolutional neural networks for 3d shape recognition. CoRR abs/1505.00880 (2015). Google ScholarDigital Library
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbig- niew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. CoRR abs/1512.00567 (2015). http://arxiv.org/abs/1512.00567Google Scholar
- Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu. 2014. Learning Fine-Grained Image Similarity with Deep Ranking. In Proc. CVPR. 1386--1393. Google ScholarDigital Library
- Artem Babenko Yandex and Victor Lempitsky. 2015. Aggregating Local Deep Features for Image Retrieval. In Proc. ICCV. 1269--1277. Google ScholarDigital Library
- Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How trans- ferable are features in deep neural networks? CoRR abs/1411.1792 (2014). http://arxiv.org/abs/1411.1792 Google ScholarDigital Library
- Amir Roshan Zamir, Tilman Wekel, Pulkit Agrawal, Colin Wei, Jitendra Malik, and Silvio Savarese. 2016. Generic 3D Representation via Pose Estimation and Matching. In Proc. ECCV. 535--553.Google ScholarCross Ref
- Matthew D. Zeiler and Rob Fergus. 2013. Visualizing and Understanding Convo- lutional Networks. CoRR abs/1311.2901 (2013). http://arxiv.org/abs/1311.2901Google Scholar
- Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba. 2014. Object Detectors Emerge in Deep Scene CNNs. CoRR abs/1412.6856 (2014). http://arxiv.org/abs/1412.6856Google Scholar
Index Terms
- Cross-modal Image-Graphics Retrieval by Neural Transfer Learning
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