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Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indi...
10/05/2020 ∙ by Katarína Tóthová, et al. ∙
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Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Object detection has witnessed significant progress by relying on large,...
08/21/2020 ∙ by Carlo Biffi, et al. ∙
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Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
The impressive performance of deep convolutional neural networks in sing...
04/14/2020 ∙ by Mateusz Michalkiewicz, et al. ∙
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DeepLPF: Deep Local Parametric Filters for Image Enhancement
Digital artists often improve the aesthetic quality of digital photograp...
03/31/2020 ∙ by Sean Moran, et al. ∙
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Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
This work investigates the task of unsupervised model personalization, a...
03/30/2020 ∙ by Matthias De Lange, et al. ∙
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A Multi-Hypothesis Classification Approach to Color Constancy
Contemporary approaches frame the color constancy problem as learning ca...
02/28/2020 ∙ by Daniel Hernandez-Juarez, et al. ∙
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A Multi-Hypothesis Approach to Color Constancy
Contemporary approaches frame the color constancy problem as learning ca...
02/28/2020 ∙ by Daniel Hernandez-Juarez, et al. ∙
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EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
Object detectors trained on fully-annotated data currently yield state o...
02/18/2020 ∙ by Linpu Fang, et al. ∙
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Continual learning: A comparative study on how to defy forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem ...
09/18/2019 ∙ by Matthias De Lange, et al. ∙
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Meta-Learning for Few-shot Camera-Adaptive Color Constancy
Digital camera pipelines employ color constancy methods to estimate an u...
11/28/2018 ∙ by Steven McDonagh, et al. ∙
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Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Surface reconstruction is a vital tool in a wide range of areas of medic...
07/30/2018 ∙ by Katarína Tóthová, et al. ∙
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Learning Conditioned Graph Structures for Interpretable Visual Question Answering
Visual Question answering is a challenging problem requiring a combinati...
06/19/2018 ∙ by Will Norcliffe-Brown, et al. ∙
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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...
06/05/2018 ∙ by Sarah Parisot, et al. ∙
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Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Data-driven brain parcellations aim to provide a more accurate represent...
03/29/2017 ∙ by Ira Ktena, et al. ∙
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Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease...
03/08/2017 ∙ by Sarah Parisot, et al. ∙
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Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several c...
03/07/2017 ∙ by Ira Ktena, et al. ∙
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Comparison of Brain Networks with Unknown Correspondences
Graph theory has drawn a lot of attention in the field of Neuroscience d...
11/15/2016 ∙ by Ira Ktena, et al. ∙
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Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016
Understanding brain connectivity in a network-theoretic context has show...
11/10/2016 ∙ by Sarah Parisot, et al. ∙
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