Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

TL;DR

This paper introduces a hierarchical context transformer (HCT) for multi-level semantic scene understanding in surgical videos, effectively modeling task relations and improving recognition accuracy with fewer parameters.

Contribution

The paper proposes a novel hierarchical context transformer with relation aggregation and inter-task contrastive learning for improved multi-level surgical scene understanding.

Findings

Outperforms state-of-the-art methods on cataract and endoscopic datasets.

Effectively models hierarchical task relations and enhances feature learning.

Achieves high accuracy with fewer parameters using HCT+.

Abstract

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition --> step recognition --> action and instrument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://github.com/Aurora-hao/HCT.