no code implementations • 22 Apr 2024 • Che-Tsung Lin, Chun Chet Ng, Zhi Qin Tan, Wan Jun Nah, Xinyu Wang, Jie Long Kew, PoHao Hsu, Shang Hong Lai, Chee Seng Chan, Christopher Zach
We also labeled texts in the extremely low-light See In the Dark (SID) and ordinary LOw-Light (LOL) datasets to allow for objective assessment of extremely low-light image enhancement through scene text tasks.
1 code implementation • 22 Apr 2024 • Jia Wei Sii, Chee Seng Chan
Contemporary makeup transfer methods primarily focus on replicating makeup from one face to another, considerably limiting their use in creating diverse and creative character makeup essential for visual storytelling.
no code implementations • 17 Jan 2024 • Win Kent Ong, Kam Woh Ng, Chee Seng Chan, Yi Zhe Song, Tao Xiang
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressive demonstrations.
1 code implementation • 10 Dec 2023 • Jiun Tian Hoe, Xudong Jiang, Chee Seng Chan, Yap-Peng Tan, Weipeng Hu
While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content.
no code implementations • 31 Aug 2023 • Sze Jue Yang, Quang Nguyen, Chee Seng Chan, Khoa D. Doan
The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications.
1 code implementation • 15 Feb 2023 • Kam Woh Ng, Xiatian Zhu, Jiun Tian Hoe, Chee Seng Chan, Tianyu Zhang, Yi-Zhe Song, Tao Xiang
However, these methods often overlook the fact that the similarity between data points in the continuous feature space may not be preserved in the discrete hash code space, due to the limited similarity range of hash codes.
1 code implementation • 3 Oct 2022 • Zhi Qin Tan, Hao Shan Wong, Chee Seng Chan
Capitalise on deep learning models, offering Natural Language Processing (NLP) solutions as a part of the Machine Learning as a Service (MLaaS) has generated handsome revenues.
1 code implementation • 1 Apr 2022 • PoHao Hsu, Che-Tsung Lin, Chun Chet Ng, Jie-Long Kew, Mei Yih Tan, Shang-Hong Lai, Chee Seng Chan, Christopher Zach
Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved.
1 code implementation • 11 Feb 2022 • Jia Huei Tan, Ying Hua Tan, Chee Seng Chan, Joon Huang Chuah
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 Transformers on natural language tasks.
1 code implementation • 7 Oct 2021 • 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 and computational cost.
2 code implementations • NeurIPS 2021 • Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, Tao Xiang
In this work, we propose a novel deep hashing model with only a single learning objective.
1 code implementation • 12 Jul 2021 • Chun Chet Ng, Akmalul Khairi Bin Nazaruddin, Yeong Khang Lee, Xinyu Wang, Yuliang Liu, Chee Seng Chan, Lianwen Jin, Yipeng Sun, Lixin Fan
With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components.
no code implementations • CVPR 2021 • Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang
Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.
no code implementations • 16 Mar 2021 • Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.
1 code implementation • 8 Feb 2021 • Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.
1 code implementation • 25 Aug 2020 • Jian Han Lim, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang
By and large, existing Intellectual Property (IP) protection on deep neural networks typically i) focus on image classification task only, and ii) follow a standard digital watermarking framework that was conventionally used to protect the ownership of multimedia and video content.
no code implementations • 20 Jun 2020 • Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, Qiang Yang
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks.
no code implementations • CVPR 2020 • Xinyu Wang, Yuliang Liu, Chunhua Shen, Chun Chet Ng, Canjie Luo, Lianwen Jin, Chee Seng Chan, Anton Van Den Hengel, Liangwei Wang
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
1 code implementation • NeurIPS 2019 • 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 these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners.
no code implementations • 17 Sep 2019 • Yipeng Sun, Zihan Ni, Chee-Kheng Chng, Yuliang Liu, Canjie Luo, Chun Chet Ng, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin
Robust text reading from street view images provides valuable information for various applications.
2 code implementations • 16 Sep 2019 • 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 these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners.
1 code implementation • 16 Sep 2019 • Chee-Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, ChuanMing Fang, Shuaitao Zhang, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin
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 recognition, and iii) scene text spotting.
1 code implementation • 28 Aug 2019 • 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.
no code implementations • 10 May 2019 • 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 subsequently, either paralyzes the network functionalities for unauthorized usages or maintain its functionalities in the presence of a verified passport.
2 code implementations • 4 Mar 2019 • Jia Huei Tan, Chee Seng Chan, Joon Huang Chuah
This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks.
no code implementations • 20 Jan 2019 • KamWoh Ng, Lixin Fan, Chee Seng Chan
Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability.
2 code implementations • 29 May 2018 • Yuen Peng Loh, Chee Seng Chan
Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i. e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.
no code implementations • 11 Nov 2017 • Ying Hua Tan, Chee Seng Chan
Automatic generation of caption to describe the content of an image has been gaining a lot of research interests recently, where most of the existing works treat the image caption as pure sequential data.
1 code implementation • 28 Oct 2017 • Chee Kheng Chng, Chee Seng Chan
Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500.
Ranked #27 on Scene Text Detection on Total-Text
no code implementations • 1 Sep 2017 • John E. Ball, Derek T. Anderson, Chee Seng Chan
Namely, we focus on theories, tools and challenges for the RS community.
2 code implementations • 31 Aug 2017 • Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, Kiyoshi Tanaka
Qualitatively, we demonstrate that ArtGAN is able to generate plausible-looking images on Oxford-102 and CUB-200, as well as able to draw realistic artworks based on style, artist, and genre.
4 code implementations • 11 Feb 2017 • Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, Kiyoshi Tanaka
This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics.
no code implementations • 20 Aug 2016 • Ying Hua Tan, Chee Seng Chan
The two levels of this model are dedicated to i) learn to generate image relevant noun phrases, and ii) produce appropriate image description from the phrases and other words in the corpus.
no code implementations • 20 Nov 2015 • Ven Jyn Kok, Mei Kuan Lim, Chee Seng Chan
Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irre- vocable crowd disasters.
1 code implementation • 28 Jun 2015 • Sue Han Lee, Chee Seng Chan, Paul Wilkin, Paolo Remagnino
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England.
no code implementations • 1 Dec 2014 • Chern Hong Lim, Ekta Vats, Chee Seng Chan
Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on.
no code implementations • 15 Oct 2014 • Mei Kuan Lim, Chee Seng Chan, Dorothy Monekosso, Paolo Remagnino
The increasing number of cameras and a handful of human operators to monitor the video inputs from hundreds of cameras leave the system ill equipped to fulfil the task of detecting anomalies.
no code implementations • 14 Oct 2014 • Mei Kuan Lim, Ven Jyn Kok, Chen Change Loy, Chee Seng Chan
This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure.
no code implementations • 14 Oct 2014 • Wai Lam Hoo, Tae-Kyun Kim, Yuru Pei, Chee Seng Chan
Image understanding is an important research domain in the computer vision due to its wide real-world applications.
no code implementations • 14 Oct 2014 • Wai Lam Hoo, Chee Seng Chan
Object recognition systems usually require fully complete manually labeled training data to train the classifier.
no code implementations • 14 Oct 2014 • Mei Kuan Lim, Chee Seng Chan, Dorothy Monekosso, Paolo Remagnino
Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model.
no code implementations • 14 Oct 2014 • Chern Hong Lim, Anhar Risnumawan, Chee Seng Chan
In this paper, we show that scene images are non-mutually exclusive, and propose the Fuzzy Qualitative Rank Classifier (FQRC) to tackle the aforementioned problems.
no code implementations • 14 Oct 2014 • Wei Ren Tan, Chee Seng Chan, Pratheepan Yogarajah, Joan Condell
A reliable human skin detection method that is adaptable to different human skin colours and illu- mination conditions is essential for better human skin segmentation.