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Found 6 papers, 2 papers with code
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph
Learning word embeddings is an essential topic in natural language processing.
Learning from Semantic Alignment between Unpaired Multiviews for Egocentric Video Recognition
To facilitate the data efficiency of multiview learning, we further perform video-text alignment for first-person and third-person videos, to fully leverage the semantic knowledge to improve video representations.
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
In digital histopathology, entire neoplasm segmentation on Whole Slide Image (WSI) of Hepatocellular Carcinoma (HCC) plays an important role, especially as a preprocessing filter to automatically exclude healthy tissue, in histological molecular correlations mining and other downstream histopathological tasks.
Learning Representational Invariances for Data-Efficient Action Recognition
Data augmentation is a ubiquitous technique for improving image classification when labeled data is scarce.
A method for detecting text of arbitrary shapes in natural scenes that improves text spotting
Our text spotting framework, called UHTA, combines UHT with the state-of-the-art text recognition system ASTER.
Ranked #11 on
Scene Text Detection
on Total-Text
Deep Neural Network for Semantic-based Text Recognition in Images
A text-recognition network interprets isolated words.
