1 code implementation • 30 Jan 2024 • Sebastian Gerard, Paul Borne-Pons, Josephine Sullivan
We construct a strong baseline method for building damage detection by starting with the highly-engineered winning solution of the xView2 competition, and gradually stripping away components.
1 code implementation • 9 Mar 2023 • David Mohlin, Josephine Sullivan
We also show that the mode of the predicted distribution outperform our regression baselines.
no code implementations • 24 Nov 2022 • Sebastian Gerard, Josephine Sullivan
Domain-specific variants of contrastive learning can construct positive pairs from two distinct in-domain images, while traditional methods just augment the same image twice.
1 code implementation • 23 Feb 2022 • Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan, Hossein Azizpour, Mårten Björkman
The number of linear regions has been studied as a proxy of complexity for ReLU networks.
1 code implementation • 19 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.
no code implementations • EACL (LANTERN) 2021 • Sebastian Bujwid, Josephine Sullivan
We study the impact of using rich and diverse textual descriptions of classes for zero-shot learning (ZSL) on ImageNet.
1 code implementation • ECCV 2020 • Federico Baldassarre, Kevin Smith, Josephine Sullivan, Hossein Azizpour
Visual relationship detection is fundamental for holistic image understanding.
no code implementations • 12 Feb 2016 • Yang Zhong, Josephine Sullivan, Hai-Bo Li
Predicting attributes from face images in the wild is a challenging computer vision problem.
no code implementations • 4 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.
no code implementations • 20 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.
no code implementations • 24 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.
no code implementations • 22 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).
no code implementations • CVPR 2014 • Vahid Kazemi, Josephine Sullivan
This paper addresses the problem of Face Alignment for a single image.
Ranked #5 on Face Alignment on AFLW2000
4 code implementations • 23 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.
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.