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Found 5 papers, 5 papers with code
MuST: Multi-Scale Transformers for Surgical Phase Recognition
Phase recognition in surgical videos is crucial for enhancing computer-aided surgical systems as it enables automated understanding of sequential procedural stages.
Ranked #1 on
Surgical phase recognition
on GraSP
Pixel-Wise Recognition for Holistic Surgical Scene Understanding
To exploit our proposed benchmark, we introduce the Transformers for Actions, Phases, Steps, and Instrument Segmentation (TAPIS) model, a general architecture that combines a global video feature extractor with localized region proposals from an instrument segmentation model to tackle the multi-granularity of our benchmark.
Ranked #2 on
Surgical phase recognition
on MISAW
STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction
This paper introduces a novel benchmark to study the impact and relationship of built environment elements on pedestrian collision prediction, intending to enhance environmental awareness in autonomous driving systems to prevent pedestrian injuries actively.
MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation
We propose Masked-Attention Transformers for Surgical Instrument Segmentation (MATIS), a two-stage, fully transformer-based method that leverages modern pixel-wise attention mechanisms for instrument segmentation.
Towards Holistic Surgical Scene Understanding
Second, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a strong baseline for surgical scene understanding.
Ranked #3 on
Surgical phase recognition
on MISAW
