Friends Across Time: Multi-Scale Action Segmentation Transformer for
  Surgical Phase Recognition
Bokai Zhang Jiayuan Meng Bin Cheng Dean Biskup Svetlana Petculescu Angela Chapman
Automatic surgical phase recognition is a core technology for modern
operating rooms and online surgical video assessment platforms. Current
state-of-the-art methods use both spatial and temporal information to tackle
the surgical phase recognition task. Building on this idea, we propose the
Multi-Scale Action Segmentation Transformer (MS-AST) for offline surgical phase
recognition and the Multi-Scale Action Segmentation Causal Transformer
(MS-ASCT) for online surgical phase recognition. We use ResNet50 or
EfficientNetV2-M for spatial feature extraction. Our MS-AST and MS-ASCT can
model temporal information at different scales with multi-scale temporal
self-attention and multi-scale temporal cross-attention, which enhances the
capture of temporal relationships between frames and segments. We demonstrate
that our method can achieve 95.26% and 96.15% accuracy on the Cholec80 dataset
for online and offline surgical phase recognition, respectively, which achieves
new state-of-the-art results. Our method can also achieve state-of-the-art
results on non-medical datasets in the video action segmentation domain.