Sarah Parisot | DeepAI

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∙ 04/04/2023

Learning to Name Classes for Vision and Language Models

Large scale vision and language models can achieve impressive zero-shot ...

0 Sarah Parisot, et al. ∙

∙ 11/09/2022

Content-Diverse Comparisons improve IQA

Image quality assessment (IQA) forms a natural and often straightforward...

0 William Thong, et al. ∙

∙ 10/07/2022

CLAD: A realistic Continual Learning benchmark for Autonomous Driving

In this paper we describe the design and the ideas motivating a new Cont...

0 Eli Verwimp, et al. ∙

∙ 04/04/2022

Re-examining Distillation For Continual Object Detection

Training models continually to detect and classify objects, from new cla...

0 Eli Verwimp, et al. ∙

∙ 12/13/2021

Long-tail Recognition via Compositional Knowledge Transfer

In this work, we introduce a novel strategy for long-tail recognition th...

0 Sarah Parisot, et al. ∙

∙ 06/11/2021

Learning Compositional Shape Priors for Few-Shot 3D Reconstruction

The impressive performance of deep convolutional neural networks in sing...

0 Mateusz Michalkiewicz, et al. ∙

∙ 10/05/2020

Probabilistic 3D surface reconstruction from sparse MRI information

Surface reconstruction from magnetic resonance (MR) imaging data is indi...

10 Katarína Tóthová, et al. ∙

∙ 08/21/2020

Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection

Object detection has witnessed significant progress by relying on large,...

0 Carlo Biffi, et al. ∙

∙ 04/14/2020

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

The impressive performance of deep convolutional neural networks in sing...

20 Mateusz Michalkiewicz, et al. ∙

∙ 03/31/2020

DeepLPF: Deep Local Parametric Filters for Image Enhancement

Digital artists often improve the aesthetic quality of digital photograp...

5 Sean Moran, et al. ∙

∙ 03/30/2020

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

This work investigates the task of unsupervised model personalization, a...

0 Matthias De Lange, et al. ∙

∙ 02/28/2020

A Multi-Hypothesis Classification Approach to Color Constancy

Contemporary approaches frame the color constancy problem as learning ca...

14 Daniel Hernandez-Juarez, et al. ∙

∙ 02/28/2020

A Multi-Hypothesis Approach to Color Constancy

Contemporary approaches frame the color constancy problem as learning ca...

4 Daniel Hernandez-Juarez, et al. ∙

∙ 02/18/2020

EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement

Object detectors trained on fully-annotated data currently yield state o...

0 Linpu Fang, et al. ∙

∙ 09/18/2019

Continual learning: A comparative study on how to defy forgetting in classification tasks

Artificial neural networks thrive in solving the classification problem ...

0 Matthias De Lange, et al. ∙

∙ 11/28/2018

Meta-Learning for Few-shot Camera-Adaptive Color Constancy

Digital camera pipelines employ color constancy methods to estimate an u...

0 Steven McDonagh, et al. ∙

∙ 07/30/2018

Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

Surface reconstruction is a vital tool in a wide range of areas of medic...

6 Katarína Tóthová, et al. ∙

∙ 06/19/2018

Learning Conditioned Graph Structures for Interpretable Visual Question Answering

Visual Question answering is a challenging problem requiring a combinati...

0 Will Norcliffe-Brown, et al. ∙

∙ 06/05/2018

Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Graphs are widely used as a natural framework that captures interactions...

0 Sarah Parisot, et al. ∙

∙ 03/29/2017

Exploring Heritability of Functional Brain Networks with Inexact Graph Matching

Data-driven brain parcellations aim to provide a more accurate represent...

0 Ira Ktena, et al. ∙

∙ 03/08/2017

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease...

0 Sarah Parisot, et al. ∙

∙ 03/07/2017

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several c...

0 Ira Ktena, et al. ∙

∙ 11/15/2016

Comparison of Brain Networks with Unknown Correspondences

Graph theory has drawn a lot of attention in the field of Neuroscience d...

0 Ira Ktena, et al. ∙

∙ 11/10/2016

Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016

Understanding brain connectivity in a network-theoretic context has show...

0 Sarah Parisot, et al. ∙