Search Results for author: Kevin P. Murphy

Found 8 papers, 2 papers with code

Modeling Uncertainty with Hedged Instance Embeddings

no code implementations ICLR 2019 Seong Joon Oh, Kevin P. Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew C. Gallagher

Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.

Clustering Metric Learning +1

Semantic Instance Segmentation via Deep Metric Learning

1 code implementation30 Mar 2017 Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.

Instance Segmentation Metric Learning +3

Im2Calories: Towards an Automated Mobile Vision Food Diary

no code implementations ICCV 2015 Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

1 code implementation ICCV 2015 George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, Alan L. Yuille

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.

Image Segmentation Segmentation +1

Variational bounds for mixed-data factor analysis

no code implementations NeurIPS 2010 Mohammad E. Khan, Guillaume Bouchard, Kevin P. Murphy, Benjamin M. Marlin

We show that EM is significantly more robust in the presence of missing data compared to treating the latent factors as parameters, which is the approach used by exponential family PCA and other related matrix-factorization methods.

Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models

no code implementations NeurIPS 2009 Baback Moghaddam, Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin

In this paper we make several contributions towards accelerating approximate Bayesian structural inference for non-decomposable GGMs.

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