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Found 9 papers, 2 papers with code
Improving Information Extraction on Business Documents with Specific Pre-Training Tasks
These tasks force the model to learn better-contextualized representations of the scanned documents.
Long-Range Transformer Architectures for Document Understanding
They are based on efficient implementations of Transformers for long sequences.
Sequence Metric Learning as Synchronization of Recurrent Neural Networks
Sequence metric learning is becoming a widely adopted approach for various applications dealing with sequential multi-variate data such as activity recognition or natural language processing and is most of the time tackled with sequence alignment approaches or representation learning.
Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm
Moreover, the trade-off between the sparsity and the accuracy is compared to other loss regularization terms based on the l1 or l2 norm as well as the SSL, NISP and GAL methods and shows that our approach is outperforming them.
2D Wasserstein Loss for Robust Facial Landmark Detection
The recent performance of facial landmark detection has been significantly improved by using deep Convolutional Neural Networks (CNNs), especially the Heatmap Regression Models (HRMs).
Routine Modeling with Time Series Metric Learning
Traditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities.
End-to-End Data Visualization by Metric Learning and Coordinate Transformation
This paper presents a deep nonlinear metric learning framework for data visualization on an image dataset.
Improving Texture Categorization with Biologically Inspired Filtering
Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms.
