SurgLQA: 可扩展的长时程外科视频问答
SurgLQA: Scalable Long-Horizon Surgical Video Question Answering
AI总结：
本文提出SurgLQA框架，通过融合时间一致性巩固和时间接地多策略扩展方法，解决长时程外科视频问答中的长程动态建模问题，提升手术流程中的推理能力。
摘要：
外科视频问答（VideoQA）提供了一个有前景的动态术中解释范式，能够为临床环境中的实时决策支持和上下文感知检索提供支持。然而，现有方法主要局限于图像或短片段，限制了其对长程手术流程中因果依赖关系的建模能力。为解决这一挑战，我们提出了SurgLQA，一个统一的长时程VideoQA框架，用于可扩展的外科推理。该框架集成了忠实时间一致性巩固（FTC），利用内在时间线索构建紧凑的长程表示，同时保持细粒度的时间保真度。进一步，我们开发了时间接地多策略扩展（TMS），一种适应性测试时间推理范式，能够在时间接地上下文中战略性地调整策略层面的推理能力。为了促进系统评估，我们重构了一个长时程结肠镜VideoQA基准，Colon-LQA，并在Colon-LQA和REAL-Colon-VQA上进行了广泛的实验。实验结果表明，我们的方法在长程推理中通过时间接地推理实现了持续的性能提升。代码链接：https://github.com/RascalGdd/SurgLQA。
英文摘要：
Surgical Video Question Answering (VideoQA) provides a promising paradigm for dynamic intraoperative interpretation, enabling real-time decision support and context-aware retrieval in clinical environments. Nevertheless, existing approaches are predominantly restricted to images or short clips, limiting their ability to model long-range procedural dynamics and causal dependencies across extended surgical workflows. To address this challenge, we propose SurgLQA, a unified long-horizon VideoQA framework for scalable surgical reasoning. This framework incorporates Faithful Temporal Consolidation (FTC), which leverages intrinsic temporal cues to construct compact long-range representations while preserving fine-grained temporal fidelity. Further, we develop Temporally-Grounded Multi-Policy Scaling (TMS), an adaptive test-time inference paradigm that strategically adjusts policy-level reasoning capacity within temporally grounded contexts. To facilitate systematic evaluation, we restructured a long-duration colonoscopy VideoQA benchmark, Colon-LQA, and conducted extensive experiments on Colon-LQA and REAL-Colon-VQA. Experimental results demonstrate that our approach achieves consistent performance gains in long-range reasoning with temporally grounded inference. Code link: https://github.com/RascalGdd/SurgLQA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
