CVPR18: Tutorial: Part 2: Weakly Supervised Learning for Computer VisionRate this post Organizers: Rodrigo Benenson Hakan Bilen Jasper Uijling Description: Deep convolutional networks have become the go-to technique for...
CVPR18: Tutorial: Part 2: Unsupervised Visual LearningRate this post Organizers: Pierre Sermanet Carl Vondrick Anelia Angelova Description: Unsupervised learning focuses on learning from vast amounts of data without manual...
CVPR18: Tutorial: Part 1: Unsupervised Visual LearningRate this post Organizers: Pierre Sermanet Carl Vondrick Anelia Angelova Description: Unsupervised learning focuses on learning from vast amounts of data without manual...
CVPR18: Tutorial: Part 1: Weakly Supervised Learning for Computer VisionRate this post Organizers: Rodrigo Benenson Hakan Bilen Jasper Uijlings Description: Deep convolutional networks have become the go-to technique for...
CVPR18: Session 3-3A: Machine Learning for Computer Vision VRate this post Orals (O3-3A) 1. [E21] StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, Yunjey Choi, Minje Choi, Munyoung...
CVPR18: Session 3-1A: Object Recognition & Scene Understanding IVRate this post Orals (O3-1A) 1. [A1] Squeeze-and-Excitation Networks, Jie Hu, Li Shen, Gang Sun 2. [A3] Revisiting Salient Object Detection:...
CVPR18: Session 2-1A: Object Recognition & Scene Understanding IIRate this post Orals (O2-1A) 1. [A1] 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare, Abhijit Kundu, Yin Li, James M. Rehg...
CVPR18: Session 2-1A: Object Recognition & Scene Understanding IIRate this post Orals (O2-1A) 1. [A1] 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare, Abhijit Kundu, Yin Li, James M. Rehg...
Regularization with Stochastic Transformations and Perturbations, NIPS 2016 SpotlightRate this post https://papers.nips.cc/paper/6333-regularization-with-stochastic-transformations-and-perturbations-for-deep-semi-supervised-learning.pdf Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets...
On Regularizing Rademacher Observation Losses, NIPS 2016 SpotlightRate this post https://papers.nips.cc/paper/6572-on-regularizing-rademacher-observation-losses.pdf It has recently been shown that supervised learning linear classifiers with two of the most popular losses, We...
