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Grab What You Need: Rethinking Complex Table Structure Recognition with
  Flexible Components Deliberation

Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation

16 March 2023
Hao Liu
Xin Li
Ming Gong
Bin Liu
Yunfei Wu
Deqiang Jiang
Yinsong Liu
Xing Sun
    LMTD
ArXivPDFHTML

Papers citing "Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation"

4 / 4 papers shown
Title
PubTables-1M: Towards comprehensive table extraction from unstructured
  documents
PubTables-1M: Towards comprehensive table extraction from unstructured documents
B. Smock
Rohith Pesala
Robin Abraham
LMTD
27
96
0
30 Sep 2021
Pix2seq: A Language Modeling Framework for Object Detection
Pix2seq: A Language Modeling Framework for Object Detection
Ting-Li Chen
Saurabh Saxena
Lala Li
David J. Fleet
Geoffrey E. Hinton
MLLM
ViT
VLM
233
341
0
22 Sep 2021
Deep Reinforcement Learning for Dialogue Generation
Deep Reinforcement Learning for Dialogue Generation
Jiwei Li
Will Monroe
Alan Ritter
Michel Galley
Jianfeng Gao
Dan Jurafsky
198
1,325
0
05 Jun 2016
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
228
31,150
0
16 Jan 2013
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