26 citations · 103 across the 19 of their papers we have counts for
21 papers
Sparse All-Layer Connector for Domain Generalised Semantic Segmentation
Ruoyu Guo, Xin Kun Lin, Maurice Pagnucco +1
Domain generalised semantic segmentation (DGSS) has recently benefited from combining multiple foundation models, such as vision-language and vision foundation models, to leverage…
Hierarchical Prompt Injector for Domain Generalization Segmentation
Xin Kun Lin, Ruoyu Guo, Jiaqi Guo +2
Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains. In contrast, structural…
NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics
Sicong Gao, Maurice Pagnucco, Tomasz Bednarz +1
Simulation has become a core infrastructure for robotics research. Unlike previous simulators, NVIDIA Isaac Sim leverages GPU acceleration to enable large-scale parallel training a…
Reinforcement Learning for Follow-the-Leader Robotic Endoscopic Navigation via Synthetic Data
Sicong Gao, Chen Qian, Laurence Xian +3
Autonomous navigation is crucial for both medical and industrial endoscopic robots, enabling safe and efficient exploration of narrow tubular environments without continuous human…
Principles2Plan: LLM-Guided System for Operationalising Ethical Principles into Plans
Tammy Zhong, Yang Song, Maurice Pagnucco
Ethical awareness is critical for robots operating in human environments, yet existing automated planning tools provide little support. Manually specifying ethical rules is labour-…
Temporal Alignment of Time Sensitive Facts with Activation Engineering
Sanjay Govindan, Maurice Pagnucco, Yang Song
Large Language Models (LLMs) are trained on diverse and often conflicting knowledge spanning multiple domains and time periods. Some of this knowledge is only valid within specific…
Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
Qingchen Tang, Lei Fan, Maurice Pagnucco +1
Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (C…
Interpretable Image Classification via Non-parametric Part Prototype Learning
Zhijie Zhu, Lei Fan, Maurice Pagnucco +1
Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as a…
Structure based SAT dataset for analysing GNN generalisation
Yi Fu, Anthony Tompkins, Yang Song +1
Satisfiability (SAT) solvers based on techniques such as conflict driven clause learning (CDCL) have produced excellent performance on both synthetic and real world industrial prob…
Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products
Bethia Sun, Maurice Pagnucco, Yang Song
Since the inception of the classicalist vs. connectionist debate, it has been argued that the ability to systematically combine symbol-like entities into compositional representati…
Dataset Distillation for Histopathology Image Classification
Cong Cong, Shiyu Xuan, Sidong Liu +3
Deep neural networks (DNNs) have exhibited remarkable success in the field of histopathology image analysis. On the other hand, the contemporary trend of employing large models and…
An annotated grain kernel image database for visual quality inspection
Lei Fan, Yiwen Ding, Dongdong Fan +4
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels. The database contains more than 350K single-kernel images w…
DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
Haoran Fan, Qi Fan, Maurice Pagnucco +1
Few-shot segmentation (FSS) aims to segment novel classes in a query image by using only a small number of supporting images from base classes. However, in cross-domain few-shot se…
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection
Lei Fan, Yiwen Ding, Dongdong Fan +3
Cereal grain plays a crucial role in the human diet as a major source of essential nutrients. Grain Appearance Inspection (GAI) serves as an essential process to determine grain qu…
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
Junyan Wang, Zhenhong Sun, Yichen Qian +5
3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences,…
Vision-based Multi-future Trajectory Prediction: A Survey
Renhao Huang, Hao Xue, Maurice Pagnucco +2
Vision-based trajectory prediction is an important task that supports safe and intelligent behaviours in autonomous systems. Many advanced approaches have been proposed over the ye…
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Ling Yang, Zhilin Huang, Yang Song +6
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and the…
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction
Lihuan Li, Maurice Pagnucco, Yang Song
Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning. Besides genera…
Explainability in Graph Neural Networks: An Experimental Survey
Peibo Li, Yixing Yang, Maurice Pagnucco +1
Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains. However, similar to all other neural networks models,…
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains
Lei Fan, Yiwen Ding, Dongdong Fan +3
Cereal grains are a vital part of human diets and are important commodities for people's livelihood and international trade. Grain Appearance Inspection (GAI) serves as one of the…
Discriminative Latent Semantic Graph for Video Captioning
Yang Bai, Junyan Wang, Yang Long +4
Video captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video. Existing generative models like encoder-decoder f…