26 citations · 93 across the 23 of their papers we have counts for
27 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…
Identifying Temporal Features within Transcoders for Time Sensitive Factual Recall
Sanjay Govindan, Yang Song, Maurice Pagnucco
Large Language Models (LLMs) suffer from temporal misalignment, often due to the contradictory nature of their training corpora. While current mitigation strategies rely on computa…
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…
P-PatchDiff: Progressive Patch Diffusion Models for Low-light Image Enhancement
Ruoyu Guo, Haonan Zhong, Maurice Pagnucco +1
Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detailed images. Patch diffusion mod…
FAIRT2V: Training-Free Debiasing for Text-to-Video Diffusion Models
Haonan Zhong, Wei Song, Tingxu Han +3
Text-to-video (T2V) diffusion models have achieved rapid progress, yet their demographic biases, particularly gender bias, remain largely unexplored. We present FairT2V, a training…
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…
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects
Lei Fan, Dongdong Fan, Zhiguang Hu +5
We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical doma…
Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection
Lei Fan, Junjie Huang, Donglin Di +4
For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource managem…
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…
Online Learning and Planning in Cognitive Hierarchies
Bernhard Hengst, Maurice Pagnucco, David Rajaratnam +2
Complex robot behaviour typically requires the integration of multiple robotic and Artificial Intelligence (AI) techniques and components. Integrating such disparate components int…
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…
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…