activity
20182025
most citedScaling Vision Transformers to 22 Billion Parameters

118 citations · 229 across the 23 of their papers we have counts for

collaborators

23 papers

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024★ 2 cited

Pretrained Visual Uncertainties

Michael Kirchhof, Mark Collier, Seong Joon Oh +1

Accurate uncertainty estimation is vital to trustworthy machine learning, yet uncertainties typically have to be learned for each task anew. This work introduces the first pretrain…

cs.CV2024

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,…

cs.CV2023

Informative Priors Improve the Reliability of Multimodal Clinical Data Classification

L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout

Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…

cs.LG2023

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…