12 citations · 47 across the 13 of their papers we have counts for
14 papers
Semantic Document Derendering: SVG Reconstruction via Vision-Language Modeling
Adam Hazimeh, Ke Wang, Mark Collier +3
Multimedia documents such as slide presentations and posters are designed to be interactive and easy to modify. Yet, they are often distributed in a static raster format, which lim…
Sketch-to-Layout: Sketch-Guided Multimodal Layout Generation
Riccardo Brioschi, Aleksandr Alekseev, Emanuele Nevali +9
Graphic layout generation is a growing research area focusing on generating aesthetically pleasing layouts ranging from poster designs to documents. While recent research has explo…
Representing Online Handwriting for Recognition in Large Vision-Language Models
Anastasiia Fadeeva, Philippe Schlattner, Andrii Maksai +4
The adoption of tablets with touchscreens and styluses is increasing, and a key feature is converting handwriting to text, enabling search, indexing, and AI assistance. Meanwhile,…
Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels
Ke Wang, Guillermo Ortiz-Jimenez, Rodolphe Jenatton +3
Label noise is a pervasive problem in deep learning that often compromises the generalization performance of trained models. Recently, leveraging privileged information (PI) -- inf…
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
Jannik Kossen, Mark Collier, Basil Mustafa +7
We introduce Three Towers (3T), a flexible method to improve the contrastive learning of vision-language models by incorporating pretrained image classifiers. While contrastive mod…
SmartChoices: Augmenting Software with Learned Implementations
Daniel Golovin, Gabor Bartok, Eric Chen +7
In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall…
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou +1
Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e.g. the ID of the a…
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier, Basil Mustafa, Efi Kokiopoulou +2
Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label…
Routing Networks with Co-training for Continual Learning
Mark Collier, Efi Kokiopoulou, Andrea Gesmundo +1
The core challenge with continual learning is catastrophic forgetting, the phenomenon that when neural networks are trained on a sequence of tasks they rapidly forget previously le…
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise
Mark Collier, Basil Mustafa, Efi Kokiopoulou +2
Datasets with noisy labels are a common occurrence in practical applications of classification methods. We propose a simple probabilistic method for training deep classifiers under…
Ranking architectures using meta-learning
Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz +3
Neural architecture search has recently attracted lots of research efforts as it promises to automate the manual design of neural networks. However, it requires a large amount of c…
Flexible Multi-task Networks by Learning Parameter Allocation
Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo +3
This paper proposes a novel learning method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharin…
General techniques for approximate incidences and their application to the camera posing problem
Dror Aiger, Haim Kaplan, Efi Kokiopoulou +2
We consider the classical camera pose estimation problem that arises in many computer vision applications, in which we are given n 2D-3D correspondences between points in the scene…
Fast Task-Aware Architecture Inference
Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz +3
Neural architecture search has been shown to hold great promise towards the automation of deep learning. However in spite of its potential, neural architecture search remains quite…