CVPR18: Session 1-2C: Machine Learning for Computer Vision II HD

Orals (O1-2C) 1. [E10] Learning to Find Good Correspondences, Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, Pascal Fua 2. [E13] OATM: Occlusion Aware Template Matching by Consensus Set Maximization, Simon Korman, Mark Milam, Stefano Soatto 3. [E16] Deep Learning of Graph Matching, Andrei Zanfir, Cristian Sminchisescu 4. [E19] Unsupervised Discovery of Object Landmarks as Structural Representations, Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, Honglak Lee Spotlights (S1-2C) 1. [E22] Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference, Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko 2. [F3] Lean Multiclass Crowdsourcing, Grant Van Horn, Steve Branson, Scott Loarie, Serge Belongie, Pietro Perona 3. [F6] Partial Transfer Learning With Selective Adversarial Networks, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan 4. [F9] Self-Supervised Feature Learning by Learning to Spot Artifacts, Simon Jenni, Paolo Favaro 5. [F12] LDMNet: Low Dimensional Manifold Regularized Neural Networks, Wei Zhu, Qiang Qiu, Jiaji Huang, Robert Calderbank, Guillermo Sapiro, Ingrid Daubechies 6. [F15] CondenseNet: An Efficient DenseNet Using Learned Group Convolutions, Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger 7. [F18] Learning Deep Descriptors With Scale-Aware Triplet Networks, Michel Keller, Zetao Chen, Fabiola Maffra, Patrik Schmuck, Margarita Chli 8. [F21] Decoupled Networks, Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song 9. [G2] Deep Adversarial Metric Learning, Yueqi Duan, Wenzhao Zheng, Xudong Lin, Jiwen Lu, Jie Zhou