29 citations · 29 across the 3 of their papers we have counts for
7 papers · 1 filter
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani +1
This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect de…
Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection
Federico Girella, Ziyue Liu, Franco Fummi +3
Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative…
Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection
Luigi Capogrosso, Federico Girella, Francesco Taioli +5
In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general,…
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
Vivek Singh Bawa, Gurkirt Singh, Francis KapingA +16
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging. The challenges come from the peculiar struct…
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
Irtiza Hasan, Francesco Setti, Theodore Tsesmelis +5
In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joi…
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Irtiza Hasan, Francesco Setti, Theodore Tsesmelis +3
Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows th…