663 citations · 829 across the 12 of their papers we have counts for
13 papers
Probabilistic 3d regression with projected huber distribution
David Mohlin, Josephine Sullivan
Estimating probability distributions which describe where an object is likely to be from camera data is a task with many applications. In this work we describe properties which we…
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs
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. For e…
Are All Linear Regions Created Equal?
Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan +2
The number of linear regions has been studied as a proxy of complexity for ReLU networks. However, the empirical success of network compression techniques like pruning and knowledg…
Probabilistic Regression with Huber Distributions
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. Our method is designed to be robus…
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions
Sebastian Bujwid, Josephine Sullivan
We study the impact of using rich and diverse textual descriptions of classes for zero-shot learning (ZSL) on ImageNet. We create a new dataset ImageNet-Wiki that matches each Imag…
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Federico Baldassarre, Kevin Smith, Josephine Sullivan +1
Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challeng…
Probabilistic orientation estimation with matrix Fisher distributions
D. Mohlin, G. Bianchi, J. Sullivan
This paper focuses on estimating probability distributions over the set of 3D rotations () using deep neural networks. Learning to regress models to the set of rotations is…
Face Attribute Prediction Using Off-the-Shelf CNN Features
Yang Zhong, Josephine Sullivan, Haibo Li
Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally on…
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Yang Zhong, Josephine Sullivan, Haibo Li
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature re…
Visual Instance Retrieval with Deep Convolutional Networks
Ali Sharif Razavian, Josephine Sullivan, Stefan Carlsson +1
This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides th…
Persistent Evidence of Local Image Properties in Generic ConvNets
Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki +3
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary informatio…
Factors of Transferability for a Generic ConvNet Representation
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan +2
Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is…
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan +1
Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indee…