Search Results for author:
Found 8 papers, 2 papers with code
Causal Prototype-inspired Contrast Adaptation for Unsupervised Domain Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery
Then, a causal prototypical contrast module is used to learn domain invariant causal features.
Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
Therefore, we claim the necessary standards for an ideal benchmark to evaluate the information extraction ability of PTLMs.
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems.
Ranked #1 on
Entity Linking
on FUNSD
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding
LayoutMask can enhance the interactions between text and layout modalities in a unified model and produce adaptive and robust multi-modal representations for downstream tasks.
Ranked #1 on
Semantic entity labeling
on FUNSD
Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory
This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.
A comprehensive benchmark analysis for sand dust image reconstruction
In this paper, we presented a comprehensive perceptual study and analysis of real-world sand dust images, then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training Convolutional Neural Networks (CNNs) and evaluating algorithms performance.
