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researcher

C. Chan

4 papers hereh-index 9297 citations16 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV1
same name
  • C. Chan — 22 papers, h 35
  • C. Chan — 18 papers, h 15
  • C. Chan — 17 papers, h 30
  • C. Chan — 11 papers, h 69
  • C. Chan — 9 papers, h 12
  • C. Chan — 8 papers

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
20202022
most citedEnd-to-End Supermask Pruning: Learning to Prune Image Captioning Models

23 citations · 37 across the 4 of their papers we have counts for

collaborators

4 papers

eess.IV2022★ 1 cited

Extremely Low-light Image Enhancement with Scene Text Restoration

Pohao Hsu, Che-Tsung Lin, Chun Chet Ng +5

Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved. However, w…

cs.CV2022

ACORT: A Compact Object Relation Transformer for Parameter Efficient Image Captioning

Jia Huei Tan, Ying Hua Tan, Chee Seng Chan +1

Recent research that applies Transformer-based architectures to image captioning has resulted in state-of-the-art image captioning performance, capitalising on the success of Trans…

cs.CV2021★ 23 cited

End-to-End Supermask Pruning: Learning to Prune Image Captioning Models

Jia Huei Tan, Chee Seng Chan, Joon Huang Chuah

With the advancement of deep models, research work on image captioning has led to a remarkable gain in raw performance over the last decade, along with increasing model complexity…

cs.CV2020★ 13 cited

On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering

Xinyu Wang, Yuliang Liu, Chunhua Shen +6

Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize. This is visible in the fact that they are vulnerable to learning coi…

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