78 citations · 171 across the 8 of their papers we have counts for
10 papers · 1 filter
Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery
Muhammad Asad, Emanuele Colleoni, Pritesh Mehta +7
Purpose: Monocular depth estimation (MDE) is vital for scene understanding in minimally invasive surgery (MIS). However, endoscopic video sequences are often contaminated by smoke,…
Zero-shot Monocular Metric Depth for Endoscopic Images
Nicolas Toussaint, Emanuele Colleoni, Ricardo Sanchez-Matilla +5
Monocular relative and metric depth estimation has seen a tremendous boost in the last few years due to the sharp advancements in foundation models and in particular transformer ba…
A spatio-temporal network for video semantic segmentation in surgical videos
Maria Grammatikopoulou, Ricardo Sanchez-Matilla, Felix Bragman +5
Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Segmentation models need to provide accurate…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
Improving filling level classification with adversarial training
Apostolos Modas, Alessio Xompero, Ricardo Sanchez-Matilla +2
We investigate the problem of classifying - from a single image - the level of content in a cup or a drinking glass. This problem is made challenging by several ambiguities caused…
Benchmark for Anonymous Video Analytics
Ricardo Sanchez-Matilla, Andrea Cavallaro
Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on compu…
Exploiting vulnerabilities of deep neural networks for privacy protection
Ricardo Sanchez-Matilla, Chau Yi Li, Ali Shahin Shamsabadi +2
Adversarial perturbations can be added to images to protect their content from unwanted inferences. These perturbations may, however, be ineffective against classifiers that were n…
Multi-view shape estimation of transparent containers
Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas +2
The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.…
ColorFool: Semantic Adversarial Colorization
Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro
Adversarial attacks that generate small L_p-norm perturbations to mislead classifiers have limited success in black-box settings and with unseen classifiers. These attacks are also…