Search Results for author: Josephine Sullivan

Found 12 papers, 4 papers with code

Probabilistic Regression with Huber Distributions

1 code implementation19 Nov 2021 David Mohlin, Gerald Bianchi, Josephine Sullivan

In this paper we describe a probabilistic method for estimating the position of an object along with its covariance matrix using neural networks.

Face Attribute Prediction Using Off-the-Shelf CNN Features

no code implementations12 Feb 2016 Yang Zhong, Josephine Sullivan, Hai-Bo Li

Predicting attributes from face images in the wild is a challenging computer vision problem.

Face Recognition General Classification

Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild

no code implementations4 Feb 2016 Yang Zhong, Josephine Sullivan, Hai-Bo Li

Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world.

Face Recognition Image Classification

Visual Instance Retrieval with Deep Convolutional Networks

no code implementations20 Dec 2014 Ali Sharif Razavian, Josephine Sullivan, Stefan Carlsson, Atsuto Maki

This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval.

Image Retrieval

Persistent Evidence of Local Image Properties in Generic ConvNets

no code implementations24 Nov 2014 Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson

Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.

General Classification

Factors of Transferability for a Generic ConvNet Representation

no code implementations22 Jun 2014 Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson

In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target).

Dimensionality Reduction Representation Learning

CNN Features off-the-shelf: an Astounding Baseline for Recognition

4 code implementations23 Mar 2014 Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Stefan Carlsson

We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13.

General Classification Image Classification +2

3D Pictorial Structures for Multiple View Articulated Pose Estimation

no code implementations CVPR 2013 Magnus Burenius, Josephine Sullivan, Stefan Carlsson

We consider the problem of automatically estimating the 3D pose of humans from images, taken from multiple calibrated views.

Pose Estimation

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