Search Results for author:
Found 17 papers, 12 papers with code
Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models
Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain.
Grounding Visual Representations with Texts for Domain Generalization
In this work, we advocate for leveraging natural language supervision for the domain generalization task.
An Embedding-Dynamic Approach to Self-supervised Learning
It also uses a purely-dynamic local dispersive force (Brownian motion) that shows improved performance over other methods and does not require knowledge of other particle coordinates.
DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting
Since only a single point is required to recognize the text, the proposed method enables text spotting without an arbitrarily-shaped detector or bounding polygon annotations.
Ranked #7 on
Text Spotting
on Total-Text
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching
Semi-structured query systems for document-oriented databases have many real applications.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
OCR-free Document Understanding Transformer
Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
Ranked #10 on
Document Image Classification
on RVL-CDIP
SelfReg: Self-supervised Contrastive Regularization for Domain Generalization
However, the performance of contrastive learning fundamentally depends on quality and quantity of negative data pairs.
Ranked #55 on
Domain Generalization
on PACS
SWAD: Domain Generalization by Seeking Flat Minima
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains.
Ranked #16 on
Domain Generalization
on TerraIncognita
Syntactic Question Abstraction and Retrieval for Data-Scarce Semantic Parsing
Deep learning approaches to semantic parsing require a large amount of labeled data, but annotating complex logical forms is costly.
Spatial Dependency Parsing for Semi-Structured Document Information Extraction
Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories.
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.
Post-OCR parsing: building simple and robust parser via BIO tagging
Parsing textual information embedded in images is important for various down- stream tasks.
Optical Character Recognition
Optical Character Recognition (OCR)
CORD: A Consolidated Receipt Dataset for Post-OCR Parsing
OCR is inevitably linked to NLP since its final output is in text.
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation.
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction
Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology.
deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks
MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them.
