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Seunghyun Park

7 papers hereh-index 5577 citations8 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author2

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • astro-ph.GA1
  • cs.CL1
  • cs.LG1
same name
  • Seunghyun Park — 10 papers, h 15
  • Seunghyun Park — 6 papers, h 3
  • Seunghyun Park — 4 papers, h 2
  • Seunghyun Park — 3 papers, h 2
  • Seunghyun Park — 3 papers, h 3
  • Seunghyun Park — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212024
most citedEvidence for strong progenitor age dependence of type Ia supernova luminosity standardization process

18 citations · 29 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

CREPE: Coordinate-Aware End-to-End Document Parser

Yamato Okamoto, Youngmin Baek, Geewook Kim +5

In this study, we formulate an OCR-free sequence generation model for visual document understanding (VDU). Our model not only parses text from document images but also extracts the…

cs.CV2022

Grounding Visual Representations with Texts for Domain Generalization

Seonwoo Min, Nokyung Park, Siwon Kim +2

Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natura…

cs.CV2022

An Embedding-Dynamic Approach to Self-supervised Learning

Suhong Moon, Domas Buracas, Seunghyun Park +2

A number of recent self-supervised learning methods have shown impressive performance on image classification and other tasks. A somewhat bewildering variety of techniques have bee…

cs.CV2022★ 6 cited

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

Seonghyeon Kim, Seung Shin, Yoonsik Kim +6

Recent end-to-end scene text spotters have achieved great improvement in recognizing arbitrary-shaped text instances. Common approaches for text spotting use region of interest poo…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.