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Papers (including subtopics)

Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting CVPR 2016 Large Scale Hard Sample Mining With Monte Carlo Tree Search CVPR 2016 Large-Scale Location Recognition and the Geometric Burstiness Problem CVPR 2016 Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling CVPR 2016 Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer CVPR 2016 Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning JMLR 2016 Latent Embeddings for Zero-Shot Classification CVPR 2016 Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition CVPR 2016 Layered Scene Decomposition via the Occlusion-CRF CVPR 2016 Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence CVPR 2016 Learning Action Maps of Large Environments via First-Person Vision CVPR 2016 Learning Activity Progression in LSTMs for Activity Detection and Early Detection CVPR 2016 Learning a Discriminative Null Space for Person Re-Identification CVPR 2016 Learning Aligned Cross-Modal Representations From Weakly Aligned Data CVPR 2016 Learning a Metric Embedding for Face Recognition using the Multibatch Method NeurIPS 2016 Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling NeurIPS 2016 Learning Attributes Equals Multi-Source Domain Generalization CVPR 2016 Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks CVPR 2016 Learning Deep Feature Representations With Domain Guided Dropout for Person Re-Identification CVPR 2016 Learning Deep Features for Discriminative Localization CVPR 2016 Learning Deep Representation for Imbalanced Classification 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