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Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model
In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
Robust Digital-Twin Localization via An RGBD-based Transformer Network and A Comprehensive Evaluation on a Mobile Dataset
The potential of digital-twin technology, involving the creation of precise digital replicas of physical objects, to reshape AR experiences in 3D object tracking and localization scenarios is significant.
Fast Autofocusing using Tiny Transformer Networks for Digital Holographic Microscopy
Tiny DL models are proposed and compared such as a tiny Vision Transformer (TViT), tiny VGG16 (TVGG) and a tiny Swin-Transfomer (TSwinT).
Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls
Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing.
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
Text recognition is a long-standing research problem for document digitalization.
Ranked #3 on
Handwritten Text Recognition
on IAM
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
Digital twin is a problem of augmenting real objects with their digital counterparts.
Nougat: Neural Optical Understanding for Academic Documents
Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs.
Optical Character Recognition
Optical Character Recognition (OCR)
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Ranked #1 on
Key Information Extraction
on SROIE
Supervised Multimodal Bitransformers for Classifying Images and Text
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.
Ranked #1 on
Natural Language Inference
on V-SNLI
(using extra training data)
Dynamic Routing Between Capsules
We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters.
Ranked #1 on
Image Classification
on MultiMNIST
