2 code implementations • 21 Aug 2023 • Qingwen Bu, Sungrae Park, Minsoo Khang, Yichuan Cheng
In light of this, we constrain the incorporation of segmentation branches to the first few decoder layers and employ progressive regression refinement in subsequent layers, achieving performance gains while minimizing computational load from the mask. Furthermore, we propose a Mask-informed Query Enhancement module.
Ranked #2 on Scene Text Detection on IC19-Art
1 code implementation • 21 Mar 2022 • Junbum Cha, Kyungjae Lee, Sungrae Park, Sanghyuk Chun
Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains.
Ranked #3 on Domain Generalization on TerraIncognita
2 code implementations • 30 Nov 2021 • Byeonghu Na, Yoonsik Kim, Sungrae Park
Furthermore, MATRN stimulates combining semantic features into visual features by hiding visual clues related to the character in the training phase.
Ranked #10 on Scene Text Recognition on ICDAR2013
1 code implementation • 10 Aug 2021 • Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park
On the other hand, this paper tackles the problem by going back to the basic: effective combination of text and layout.
Ranked #5 on Relation Extraction on FUNSD
no code implementations • 23 Jul 2021 • Junyeop Lee, Yoonsik Kim, Seonghyeon Kim, Moonbin Yim, Seung Shin, Gayoung Lee, Sungrae Park
Scene text editing (STE), which converts a text in a scene image into the desired text while preserving an original style, is a challenging task due to a complex intervention between text and style.
1 code implementation • 20 Jul 2021 • Moonbin Yim, Yoonsik Kim, Han-Cheol Cho, Sungrae Park
For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world.
4 code implementations • NeurIPS 2021 • Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, Sungrae Park
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains.
Ranked #21 on Domain Generalization on TerraIncognita
1 code implementation • 5 Feb 2021 • Mingi Ji, Byeongho Heo, Sungrae Park
Knowledge distillation extracts general knowledge from a pre-trained teacher network and provides guidance to a target student network.
Ranked #36 on Knowledge Distillation on ImageNet
no code implementations • 1 Jan 2021 • Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park
Although the recent advance in OCR enables the accurate extraction of text segments, it is still challenging to extract key information from documents due to the diversity of layouts.
1 code implementation • COLING 2020 • Sungrae Park, Geewook Kim, Junyeop Lee, Junbum Cha, Ji-Hoon Kim, Hwalsuk Lee
This paper introduces a method that efficiently reduces the computational cost and parameter size of Transformer.
no code implementations • ECCV 2020 • Youngmin Baek, Seung Shin, Jeonghun Baek, Sungrae Park, Junyeop Lee, Daehyun Nam, Hwalsuk Lee
This architecture is formed by utilizing detection outputs in the recognizer and propagating the recognition loss through the detection stage.
1 code implementation • 11 Jun 2020 • Youngmin Baek, Daehyun Nam, Sungrae Park, Junyeop Lee, Seung Shin, Jeonghun Baek, Chae Young Lee, Hwalsuk Lee
We believe that our metrics can play a key role in developing and analyzing state-of-the-art text detection and recognition methods.
2 code implementations • 10 Oct 2019 • Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee
Scene text recognition (STR) is the task of recognizing character sequences in natural scenes.
Ranked #3 on Scene Text Recognition on ICDAR 2003
no code implementations • 25 Sep 2019 • Sungrae Park, Geewook Kim, Junyeop Lee, Junbum Cha, Ji-Hoon Kim Hwalsuk Lee
When compared to Transformers with a comparable number of parameters and time complexity, the proposed model shows better performance.
1 code implementation • NeurIPS Workshop Document_Intelligen 2019 • Wonseok Hwang, Seonghyeon Kim, Minjoon Seo, Jinyeong Yim, Seunghyun Park, Sungrae Park, Junyeop Lee, Bado Lee, Hwalsuk Lee
Parsing textual information embedded in images is important for various down- stream tasks.
Optical Character Recognition Optical Character Recognition (OCR)
1 code implementation • 26 Apr 2019 • Kyungwoo Song, Mingi Ji, Sungrae Park, Il-Chul Moon
The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests.
2 code implementations • 22 Apr 2019 • Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, Il-Chul Moon
Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs).
13 code implementations • ICCV 2019 • Jeonghun Baek, Geewook Kim, Junyeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh, Hwalsuk Lee
Many new proposals for scene text recognition (STR) models have been introduced in recent years.
Ranked #7 on Scene Text Recognition on ICDAR 2003
1 code implementation • ICLR 2019 • Weonyoung Joo, Wonsung Lee, Sungrae Park, Il-Chul Moon
The experimental results show that 1) DirVAE models the latent representation result with the best log-likelihood compared to the baselines; and 2) DirVAE produces more interpretable latent values with no collapsing issues which the baseline models suffer from.
3 code implementations • 12 Jul 2017 • Sungrae Park, Jun-Keon Park, Su-Jin Shin, Il-Chul Moon
Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks.