10 papers
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-…
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…
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…
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…