Rodner, E.[Erik]
Co Author Listing * Active learning and discovery of object categories in the presence of unnameable instances
* Approximations of Gaussian Process Uncertainties for Visual Recognition Problems
* As Time Goes by: Anytime Semantic Segmentation with Iterative Context Forests
* Asymmetric and Category Invariant Feature Transformations for Domain Adaptation
* Beyond thinking in common categories: Predicting obstacle vulnerability using large random codebooks
* Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates
* Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception
* Decoupled Semantic Prototypes enable learning from diverse annotation types for semi-weakly segmentation in expert-driven domains
* Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
* Divergence-Based One-Class Classification Using Gaussian Processes
* Efficient semantic segmentation with Gaussian processes and histogram intersection kernels
* Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
* Exemplar-Specific Patch Features for Fine-Grained Recognition
* Fast Approach for Pixelwise Labeling of Facade Images, A
* Fine-grained classification of identity document types with only one example
* Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
* Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios
* Generalized Orderless Pooling Performs Implicit Salient Matching
* Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features
* Graph-Constrained Contrastive Regularization for Semi-weakly Volumetric Segmentation
* Impatient DNNs: Deep Neural Networks with Dynamic Time Budgets
* Instance-Weighted Transfer Learning of Active Appearance Models
* Kernel Null Space Methods for Novelty Detection
* Labeling Examples That Matter: Relevance-Based Active Learning with Gaussian Processes
* Large-Scale Active Learning with Approximations of Expected Model Output Changes
* Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels
* Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks
* Large-scale gaussian process multi-class classification for semantic segmentation and facade recognition
* Learning with Few Examples by Transferring Feature Relevance
* Learning with few examples for binary and multiclass classification using regularization of randomized trees
* Local Novelty Detection in Multi-class Recognition Problems
* Multi-person Tracking-by-Detection Based on Calibrated Multi-camera Systems
* Multiple kernel Gaussian process classification for generic 3D object recognition
* Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
* Nonparametric Part Transfer for Fine-Grained Recognition
* One-Class Classification with Gaussian Processes
* One-Shot Learning of Object Categories Using Dependent Gaussian Processes
* Part Detector Discovery in Deep Convolutional Neural Networks
* Randomized Probabilistic Latent Semantic Analysis for Scene Recognition
* Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels
* Selecting Influential Examples: Active Learning with Expected Model Output Changes
* Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach
* Semi-supervised Domain Adaptation with Instance Constraints
* Vegetation Segmentation in Cornfield Images Using Bag of Words
* Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization, The
* Workshop on Interactive and Adaptive Learning in an Open World
Includes: Rodner, E.[Erik] Rodner, E.[Erid]
46 for Rodner, E.
Rodney, G.G.
Co Author Listing * Automatic Ca2+ Sparks Detection System
* Ca2+ Sparks Detection and Classification using Gaussian-Mexican Hat Wavelet
Includes: Rodney, G.G. Rodney, G.G.[George G.]