Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks. To address this challenge, we introduce CliPPER (Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition), a novel video-language pretraining framework trained on surgical lecture videos. Our method is designed for fine-grained temporal video-text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long-form surgical videos. Specifically, we propose Contextual Video-Text Contrastive Learning (VTC_CTX) and Clip Order Prediction (COP) pretraining objectives, both of which leverage temporal and contextual dependencies to enhance local video understanding. In addition, we incorporate a Cycle-Consistency Alignment over video-text matches within the same surgical video to enforce bidirectional consistency and improve overall representation coherence. Moreover, we introduce a more refined alignment loss, Frame-Text Matching (FTM), to improve the alignment between video frames and text. As a result, our model establishes a new state-of-the-art across multiple public surgical benchmarks, including zero-shot recognition of phases, steps, instruments, and triplets. The source code and pretraining captions can be found at https://github.com/CAMMA-public/CliPPER.

翻译：视频-语言基础模型已被证明在广泛任务的零样本应用中具有高效性。其中，术中手术领域尤为具有挑战性，该领域标签数据稀缺，且复杂下游任务通常需要精确的时间理解。为解决这一难题，我们提出了CliPPER（面向长段术中手术视频事件识别的情境视频-语言预训练），这是一个基于手术教学视频训练的新型视频-语言预训练框架。该方法专为细粒度视频-文本时间识别设计，并引入多项创新预训练策略以提升长段手术视频中的多模态对齐能力。具体而言，我们提出了情境视频-文本对比学习（VTC_CTX）和片段顺序预测（COP）预训练目标，二者均利用时间与上下文依赖关系增强局部视频理解。此外，我们在同一手术视频内引入视频-文本匹配的循环一致性对齐，强制实现双向一致性并提升整体表征连贯性。更进一步，我们提出一种更精细的对齐损失——帧-文本匹配（FTM），以改进视频帧与文本之间的对齐效果。最终，我们的模型在多个公开手术基准测试中确立了新最优性能，涵盖阶段、步骤、器械及三元组的零样本识别任务。源代码与预训练标注文本详见 https://github.com/CAMMA-public/CliPPER。