Computer Vision

The Maximal Causes of Natural Scenes are Edge Filters NeurIPS 2010 Tiled convolutional neural networks NeurIPS 2010 Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models NeurIPS 2010 An Additive Latent Feature Model for Transparent Object Recognition NeurIPS 2009 Bayesian estimation of orientation preference maps NeurIPS 2009 Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships NeurIPS 2009 Bilinear classifiers for visual recognition NeurIPS 2009 Efficient Match Kernel between Sets of Features for Visual Recognition NeurIPS 2009 Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition NeurIPS 2009 Measuring Invariances in Deep Networks NeurIPS 2009 No evidence for active sparsification in the visual cortex NeurIPS 2009 Nonlinear Learning using Local Coordinate Coding NeurIPS 2009 Occlusive Components Analysis NeurIPS 2009 Region-based Segmentation and Object Detection NeurIPS 2009 Semi-Supervised Learning in Gigantic Image Collections NeurIPS 2009 Slow, Decorrelated Features for Pretraining Complex Cell-like Networks NeurIPS 2009 Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing NeurIPS 2009 The 'tree-dependent components' of natural scenes are edge filters NeurIPS 2009 Deep Learning with Kernel Regularization for Visual Recognition NeurIPS 2008 Dynamic visual attention: searching for coding length increments NeurIPS 2008 Efficient Exact Inference in Planar Ising Models NeurIPS 2008 Learning Hybrid Models for Image Annotation with Partially Labeled Data NeurIPS 2008 Multi-Level Active Prediction of Useful Image Annotations for Recognition NeurIPS 2008 Multiscale Random Fields with Application to Contour Grouping NeurIPS 2008 Recursive Segmentation and Recognition Templates for 2D Parsing NeurIPS 2008