Computer Vision

Learning Deep Representation for Imbalanced Classification CVPR 2016 Learning Deep Representations of Fine-Grained Visual Descriptions CVPR 2016 Learning Deep Structure-Preserving Image-Text Embeddings CVPR 2016 Learning Dense Correspondence via 3D-Guided Cycle Consistency CVPR 2016 Learning End-to-end Video Classification with Rank-Pooling ICML 2016 Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs NeurIPS 2016 Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions CVPR 2016 Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees CVPR 2016 Learning Parametric Sparse Models for Image Super-Resolution NeurIPS 2016 Learning Physical Intuition of Block Towers by Example ICML 2016 Learning Reconstruction-Based Remote Gaze Estimation CVPR 2016 Learning Relaxed Deep Supervision for Better Edge Detection CVPR 2016 Learning Sensor Multiplexing Design through Back-propagation NeurIPS 2016 Learning shape correspondence with anisotropic convolutional neural networks NeurIPS 2016 Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks CVPR 2016 Learning Structured Inference Neural Networks With Label Relations CVPR 2016 Learning Temporal Regularity in Video Sequences CVPR 2016 Learning to Co-Generate Object Proposals With a Deep Structured Network CVPR 2016 Learning to Localize Little Landmarks CVPR 2016 Learning to Match Aerial Images With Deep Attentive Architectures CVPR 2016 Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation CVPR 2016 Learning Transferrable Knowledge for Semantic Segmentation With Deep Convolutional Neural Network CVPR 2016 Learning under uncertainty: a comparison between R-W and Bayesian approach NeurIPS 2016 Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016 Learning What and Where to Draw NeurIPS 2016