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20172026
most citedWhat Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

73 citations · 123 across the 14 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.CV2019

On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention

Junyeop Lee, Sungrae Park, Jeonghun Baek +3

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to re…

cs.IR2019★ 1 cited

Hierarchical Context enabled Recurrent Neural Network for Recommendation

Kyungwoo Song, Mingi Ji, Sungrae Park +1

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the tran…

cs.LG2019★ 1 cited

Adversarial Dropout for Recurrent Neural Networks

Sungrae Park, Kyungwoo Song, Mingi Ji +2

Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques…

cs.CV2019★ 73 cited

What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

Jeonghun Baek, Geewook Kim, Junyeop Lee +5

Many new proposals for scene text recognition (STR) models have been introduced in recent years. While each claim to have pushed the boundary of the technology, a holistic and fair…

cs.LG2019

Dirichlet Variational Autoencoder

Weonyoung Joo, Wonsung Lee, Sungrae Park +1

This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabil…