Search Results for author: Kam Woh Ng

Found 13 papers, 8 papers with code

IPR-NeRF: Ownership Verification meets Neural Radiance Field

no code implementations17 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.

DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination

1 code implementation27 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.

Disentanglement Novel Concepts

Unsupervised Hashing with Similarity Distribution Calibration

1 code implementation15 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.

Deep Hashing Image Retrieval

Large-Scale Product Retrieval with Weakly Supervised Representation Learning

1 code implementation1 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.

Multi-Label Classification Representation Learning +2

Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks

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.

Image Generation Image Super-Resolution +1

Ternary Hashing

no code implementations16 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.

Retrieval

Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack

1 code implementation8 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.

Image Generation Image Super-Resolution +1

Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness

no code implementations27 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.

Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection

1 code implementation25 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.

Image Captioning Image Classification

Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity 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.

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

2 code implementations16 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.

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