Paper Stack for Yanwu

[kayhan-batmanghelich]

Self-Supervised Learning

• Self-Supervised Relative Depth Learning for Urban Scene Understanding
• Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
• Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning
• ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
• ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
• Self-supervised Knowledge Distillation Using Singular Value Decomposition
• Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
• Cross-Domain Self-Supervised Multi-Task Feature Learning Using Synthetic Imagery
• Towards Human-Machine Cooperation: Self-Supervised Sample Mining for Object Detection
• Self-Supervised Multi-Level Face Model Learning for Monocular Reconstruction at Over 250 Hz
• Self-Supervised Feature Learning by Learning to Spot Artifacts
• Self-Supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
• Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
• Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning
• Improvements to Context Based Self-Supervised Learning
• Boosting Self-Supervised Learning via Knowledge Transfer

Few Shot Learning

• Dynamic Conditional Networks for Few-Shot Learning
• Neural Graph Matching Networks for Fewshot 3D Action Recognition
• Compound Memory Networks for Few-shot Video Classification
• Few-Shot Human Motion Prediction via
  Meta-Learning
• Learning to Compare: Relation Network for Few-Shot Learning
• Dynamic Few-Shot Visual Learning Without Forgetting
• Few-Shot Image Recognition by Predicting Parameters From Activations
• Multi-Content GAN for Few-Shot Font Style Transfer

Point cloud

• Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
• Point Cloud GAN
• Learning Representations and Generative Models for 3D Point Clouds
• Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
• FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
• Recurrent Slice Networks for 3D Segmentation of Point Clouds
• SO-Net: Self-Organizing Network for Point Cloud Analysis
• Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
  
• PointCNN: Convolution On X -Transformed Points