13 citations · 24 across the 11 of their papers we have counts for
11 papers
Chirpy3D: Part-Aware Multi-View Diffusion for Creative Fine-Grained Object Generation
Kam Woh Ng, Jing Yang, Jia Wei Sii +5
Understanding and generating the fine-grained structure of objects -- such as birds with species-specific beaks, wings, and tails -- is a long-standing challenge in computer vision…
Learning Flow Fields in Attention for Controllable Person Image Generation
Zijian Zhou, Shikun Liu, Xiao Han +11
Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose. However, prior…
PartCraft: Crafting Creative Objects by Parts
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
This paper propels creative control in generative visual AI by allowing users to "select". Departing from traditional text or sketch-based methods, we for the first time allow user…
ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitat…
IPR-NeRF: Ownership Verification meets Neural Radiance Field
Win Kent Ong, Kam Woh Ng, Chee Seng Chan +2
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 impressi…
DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Recent text-to-image (T2I) generative models allow for high-quality synthesis following either text instructions or visual examples. Despite their capabilities, these models face l…
Unsupervised Hashing with Similarity Distribution Calibration
Kam Woh Ng, Xiatian Zhu, Jiun Tian Hoe +4
Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often over…
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang +3
A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints…
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng +2
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) ha…
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…
A Universal Logic Operator for Interpretable Deep Convolution Networks
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. Different cho…