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 • 27 Nov 2023 • Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song, Tao Xiang
To bridge this gap, we introduce a novel task, Virtual Creatures Generation: Given a set of unlabeled images of the target concepts (e. g., 200 bird species), we aim to train a T2I model capable of creating new, hybrid concepts within diverse backgrounds and contexts.
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 • 1 Aug 2022 • Xiao Han, Kam Woh Ng, Sauradip Nag, Zhiyu Qu
Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem.
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.
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.
no code implementations • 27 Nov 2020 • Yilun Jin, Lixin Fan, Kam Woh Ng, Ce Ju, Qiang Yang
Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed.
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.
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.
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.