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Xin Tang

8 papers hereh-index 5128 citations11 works total

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

author position
  • first author1
  • middle author7

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG2
  • cs.IR1
  • cs.SD1
same name
  • Xin Tang — 13 papers, h 6
  • Xin Tang — 6 papers, h 4
  • Xin Tang — 5 papers, h 3
  • Xin Tang — 4 papers, h 3
  • Xin Tang — 2 papers, h 3
  • Xin Tang — 2 papers, h 5

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
20202023
most citedLearning Graph Normalization for Graph Neural Networks

12 citations · 38 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023

Filter Pruning via Filters Similarity in Consecutive Layers

Xiaorui Wang, Jun Wang, Xin Tang +3

Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels i…

cs.CV2021★ 11 cited

Visual-Semantic Transformer for Scene Text Recognition

Xin Tang, Yongquan Lai, Ying Liu +2

Modeling semantic information is helpful for scene text recognition. In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (V…

cs.CV2021★ 7 cited

PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex

Yelin He, Xianbiao Qi, Jiaquan Ye +5

This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. This competition has two sub-tasks: Table Structure Reconstruction (…

cs.CV2020★ 6 cited

Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition

Bingcong Li, Xin Tang, Xianbiao Qi +2

Recently, inspired by Transformer, self-attention-based scene text recognition approaches have achieved outstanding performance. However, we find that the size of model expands rap…

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