Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

• URL: http://arxiv.org/abs/2502.15184v1
• Date: Fri, 21 Feb 2025 03:36:16 GMT
• Title: Hierarchical Context Transformer for Multi-level Semantic Scene Understanding
• Abstract summary: We propose to represent the tasks set as multi-level semantic scene understanding (MSSU)<n>For this target, we propose a novel hierarchical context transformer (HCT) network.<n>Experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method.
• Score: 37.35498412336018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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