We study supervised action segmentation whose goal is to predict framewise action labels
of a video. To capture temporal dependencies over long horizons prior works either improve …

We present a new egocentric procedural error dataset containing videos with various types
of errors as well as normal videos and propose a new framework for procedural error …

Automatic surgical workflow analysis, which aims to recognize the ongoing surgical events
in videos, is fundamental for develo** context-aware computer-assisted systems. This …

We address the problem of online action segmentation for egocentric procedural task
videos. While previous studies have mostly focused on offline action segmentation where …

Surgical practice involves complex visual interpretation, procedural skills, and advanced
medical knowledge, making surgical vision-language pretraining (VLP) particularly …

Existing state-of-the-art methods for surgical phase recognition either rely on the extraction
of spatial-temporal features at a short-range temporal resolution or adopt the sequential …

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in
telesurgery and telementoring. However, its potential to enhance data transmission …

Batch Normalization's (BN) unique property of depending on other samples in a batch is
known to cause problems in several tasks, including sequential modeling. Yet, BN-related …

Objective: To enable context-aware computer assistance in the operating room of the future,
cognitive systems need to understand automatically which surgical phase is being …

Phase recognition in surgical videos is crucial for enhancing computer-aided surgical
systems as it enables automated understanding of sequential procedural stages. Existing …