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Generative modelling (GM) has advanced significantly in recent years, especially in computer vision, where it is used for various purposes, from supplementing limited sample datasets to creating art. As GMs become more integrated into our daily activities, discussions about their efficacy are becoming more prevalent. This is largely due to the potential biases they may contain, which could then influence downstream tasks and proliferate biases in society. In this dissertation, we make important contributions in improving fairness in generative models by identifying and addressing constraints which may limit their broader adoption. […]

Understanding and analyzing animal behaviors is crucial for gaining profound insights into the health, needs, and overall well-being of the animal. This involves measuring and monitoring factors such as size, growth, poses, and actions. The analysis of animal behavior holds significant importance in a wide range of domains and industries, such as livestock farming, veterinary sciences, scientific research, ecological and conservation studies. […]

Language models are critical to the advancement of natural language processing and general artificial intelligence. In this thesis, we aim to address the limitations of language models, particularly focusing on the exposure bias in Autoregressive (AR) models and the label bias in Non-Autoregressive (NAR) models. […]

Congratulations to PhD student Hee Ming Shan for obtaining SDSC Dissertion Research Fellowship 2023

Semantic segmentation is a fundamental and important task in computer vision, which aims to classify each pixel in an image. The rapid development of deep learning has significantly advanced semantic segmentation and improved the accuracy, promoting its application in fields with high accuracy requirements for pixel-level prediction, such as autonomous driving and medical diagnosis. Current works for semantic segmentation are typically based on a standard setup that all data is accessible beforehand and can be learned simultaneously. […]

Congratulations to Assistant Professor Liu Jun and his PhD Student Qu Haoxuan for winning PREMIA Best Student Paper Award

Urbanization has given rise to increasingly complex systems that sustain and manage the lives of urban residents. As populations grow, the challenges of managing traffic, overcrowding, and environmental challenges escalate. These complexities place stress on urban ecosystems, demanding innovative solutions to ensure smooth and sustainable operations.

Congratulations to Srilalitha Gopalakrishnan, ASD PhD candidate, for her appointment as President of the Singapore Institute of Landscape Architects (SILA), 2021-2023

Congratulations to Associate Professor Liu Xiaogang’s PhD Student in winning the Royal Society of Chemistry (RSC) Excellent Student Award

ISTD PhD Oral Defence Seminar by Hu Zhiqiang – This thesis advances the computational understanding and manipulation of text styles
through three interconnected pillars: (1) Text Style Transfer (TST); (2) Authorship Attribution (AA); and (3) Authorship Verification (AV), determining whether two texts share the same authorship.