# HTT-Net: Hierarchical Text-guided Transition Modeling for Surgical Video Phase Recognition

arXiv: https://arxiv.org/abs/2607.16787 · HTML: https://mlsanity.com/paper/2607.16787/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 85 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Metrics Matter in Surgical Phase Recognition](https://mlsanity.com/paper/2305.13961/) (2023)
- [CreatiParser: Generative Image Parsing of Raster Graphic Designs into Editable Layers](https://mlsanity.com/paper/2604.19632/) (2026)
- [EmoVerse: A MLLMs-Driven Emotion Representation Dataset for Interpretable Visual Emotion Analysis](https://mlsanity.com/paper/2511.12554/) (2025)
- [FACE-net: Factual Calibration and Emotion Augmentation for Retrieval-enhanced Emotional Video Captioning](https://mlsanity.com/paper/2603.17455/) (2026)
- [SR-Mamba: Effective Surgical Phase Recognition with State Space Model](https://mlsanity.com/paper/2407.08333/) (2024)
- [A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation](https://mlsanity.com/paper/2604.18988/) (2026)
- [Towards Accurate Emotion-Attributed Video Captioning via Fine-grained Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2606.08566/) (2026)
- [EmoStyle: Affective Conditioning of Style-Specialist Experts for Emotional Image Generation](https://mlsanity.com/paper/2607.10165/) (2026)
- [HistLLM: A Unified Framework for LLM-Based Multimodal Recommendation with User History Encoding and Compression](https://mlsanity.com/paper/2504.10150/) (2025)
- [Geometry-aware Gaussian Prior and Axial Attention for Cervical Cytology Image Classification](https://mlsanity.com/paper/2607.10278/) (2026)

## Abstract (arXiv)

Surgical video phase recognition is a fundamental task in computer-assisted intervention, supporting workflow understanding, intraoperative guidance, and surgical quality assessment. Although recent visual-temporal models have achieved promising progress, accurate and temporally coherent phase recognition remains challenging due to local visual ambiguity, transient prediction noise, and insufficient use of procedural semantics. To address these challenges, we propose HTT-Net, a Hierarchical Text-guided Transition modeling Network for surgical video phase recognition. The key idea is to introduce structured surgical semantic knowledge into phase-aware segment construction and semantic refinement. Specifically, we construct a hierarchical surgical semantic memory with intra-phase descriptions, inter-phase transition descriptions, and fine-grained semantic units. Based on this memory, the proposed Transition-Aware Segment Construction (TAS-Con) organizes frame-level evidence into coherent segment representations and handles boundary clips with inter-phase transition descriptions. Furthermore, we introduce Transition-Aware Segment Calibration (TAS-Calib), which calibrates phase-aware segment representations through hierarchical surgical semantics and improves discrimination under visual ambiguity without dense frame-level vision-language fusion. Experiments on Cholec80 and LCRS-100 demonstrate the effectiveness of HTT-Net for robust surgical video phase recognition.