Search Results for author: Seunghyun Park

Found 17 papers, 12 papers with code

Grounding Visual Representations with Texts for Domain Generalization

1 code implementation21 Jul 2022 Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, Jinkyu Kim

In this work, we advocate for leveraging natural language supervision for the domain generalization task.

Domain Generalization

An Embedding-Dynamic Approach to Self-supervised Learning

no code implementations7 Jul 2022 Suhong Moon, Domas Buracas, Seunghyun Park, Jinkyu Kim, John Canny

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.

Classification Image Classification +7

DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting

no code implementations10 Mar 2022 Seonghyeon Kim, Seung Shin, Yoonsik Kim, Han-Cheol Cho, Taeho Kil, Jaeheung Surh, Seunghyun Park, Bado Lee, Youngmin Baek

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.

Text Spotting

OCR-free Document Understanding Transformer

4 code implementations30 Nov 2021 Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park

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.

Document Image Classification document understanding +3

Syntactic Question Abstraction and Retrieval for Data-Scarce Semantic Parsing

no code implementations AKBC 2020 Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Minjoon Seo

Deep learning approaches to semantic parsing require a large amount of labeled data, but annotating complex logical forms is costly.

Retrieval Semantic Parsing

Spatial Dependency Parsing for Semi-Structured Document Information Extraction

1 code implementation Findings (ACL) 2021 Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Sohee Yang, Minjoon Seo

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.

Dependency Parsing

Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information

1 code implementation25 Nov 2019 Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.

Language Modelling Masked Language Modeling

A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

5 code implementations4 Feb 2019 Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Minjoon Seo

We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.

Semantic Parsing

Deep Recurrent Neural Network-Based Identification of Precursor microRNAs

1 code implementation NeurIPS 2017 Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

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

no code implementations29 Apr 2016 Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology.

Feature Engineering

deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks

1 code implementation30 Mar 2016 Byunghan Lee, Junghwan Baek, Seunghyun Park, Sungroh Yoon

MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them.

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