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20152023
most citedCOMIC: Towards A Compact Image Captioning Model with Attention

47 citations · 65 across the 7 of their papers we have counts for

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cs.CR20193 cited

[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks

Lixin Fan, Kam Woh Ng, Chee Seng Chan

With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect t…

cs.CV2019

ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT

Yipeng Sun, Zihan Ni, Chee-Kheng Chng +9

Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavi…

cs.CV2019

ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)

Chee-Kheng Chng, Yuliang Liu, Yipeng Sun +11

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recogn…

cs.CV2019

Image Captioning with Sparse Recurrent Neural Network

Jia Huei Tan, Chee Seng Chan, Joon Huang Chuah

Recurrent Neural Network (RNN) has been widely used to tackle a wide variety of language generation problems and are capable of attaining state-of-the-art (SOTA) performance. Howev…

cs.CR20195 cited

Digital Passport: A Novel Technological Strategy for Intellectual Property Protection of Convolutional Neural Networks

Lixin Fan, KamWoh Ng, Chee Seng Chan

In order to prevent deep neural networks from being infringed by unauthorized parties, we propose a generic solution which embeds a designated digital passport into a network, and…

cs.CV201947 cited

COMIC: Towards A Compact Image Captioning Model with Attention

Jia Huei Tan, Chee Seng Chan, Joon Huang Chuah

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale natura…