Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization
Proposes a context-aware pseudo-label scoring framework for zero-shot video summarization, achieving 57.58 F1 on SumMe.
Key Findings
Methodology
This paper introduces a zero-shot video summarization framework combining pseudo labels and structured multi-dimensional rubrics. Using a small set of human annotations, high-confidence pseudo labels are generated and abstracted into evaluation criteria. During inference, boundary scenes are scored solely on their descriptions, while intermediate scenes incorporate summaries of neighboring scenes to evaluate narrative continuity. The approach employs structured prompts to guide large language models, avoiding parameter tuning. Experiments on SumMe, TVSum, and QFVS show stable improvements, with F1 scores of 57.58, 63.05, and 53.79 respectively, surpassing baseline methods.
Key Results
- On SumMe, F1 score reaches 57.58, outperforming the baseline 56.73 (+0.85); TVSum achieves 63.05, surpassing 62.21 (+0.84); QFVS reaches 53.79, above 53.42 (+0.37).
- The method demonstrates robustness and cross-dataset generalization, validating the effectiveness of pseudo labels and contextual prompts.
- Performance remains stable across different video types and query-driven tasks, indicating broad applicability.
Significance
This work addresses the limitations of supervised and unsupervised methods by providing a training-free, interpretable, and adaptable framework. It leverages minimal supervision to generate dataset-specific evaluation criteria, improving stability and interpretability. The approach enhances the practical deployment of zero-shot models in diverse real-world scenarios, reducing reliance on extensive annotations and enabling scalable content summarization.
Technical Contribution
The main innovation lies in integrating structured, multi-dimensional evaluation rubrics with pseudo labels, guided by contextual prompts. The pipeline from limited annotations to scene scoring involves pseudo label generation, rubric abstraction, and prompt design, resulting in a stable, interpretable scoring system. This contrasts with prior methods that rely heavily on handcrafted prompts or unsupervised heuristics, offering a more generalizable and explainable solution.
Novelty
This is the first work to embed structured, multi-criteria evaluation schemas into zero-shot video summarization, combining pseudo labels with contextualized large language model prompts. Unlike previous template-dependent approaches, it learns dataset-specific evaluation criteria, significantly reducing prompt sensitivity and improving stability and interpretability.
Limitations
- Relies on limited annotated samples, which may limit performance in highly diverse or unseen domains.
- Scene boundary detection and description quality directly impact scoring accuracy, potentially propagating errors.
- Computational costs for large models and long videos remain high; efficiency improvements are needed for real-time applications.
Future Work
Future directions include integrating multimodal cues (audio, text), enhancing scene boundary detection, and developing more efficient inference techniques. Exploring user-in-the-loop customization and extending to multimodal content beyond videos are promising avenues.
AI Executive Summary
The exponential growth of video content across social media, surveillance, and educational platforms has created an urgent need for efficient content summarization. Traditional supervised methods, though effective within their training domains, demand extensive annotations and struggle with cross-dataset generalization. Unsupervised approaches, while annotation-free, often fail to capture high-level semantics and narrative coherence. Recent advances leverage large language models (LLMs) for zero-shot video summarization, but these are highly sensitive to prompt design and dataset normalization, leading to instability.
This paper introduces a novel framework that combines pseudo-labeling and structured evaluation rubrics to enable stable, interpretable, and dataset-adaptive zero-shot video summarization. The approach begins with a small set of human annotations, from which high-confidence pseudo labels are generated. These labels are abstracted into multi-dimensional evaluation criteria, forming a structured rubric. During inference, boundary scenes are scored solely on their descriptions, while intermediate scenes incorporate summaries of neighboring scenes to assess narrative continuity. This contextual prompting guides large language models to produce consistent importance scores without parameter tuning.
Extensive experiments on SumMe, TVSum, and QFVS datasets demonstrate that the proposed method surpasses baseline zero-shot models, achieving F1 scores of 57.58, 63.05, and 53.79 respectively. The results highlight the framework’s robustness, stability, and broad applicability across different video types and tasks. By internalizing dataset-specific evaluation criteria and leveraging contextual information, this method offers a scalable, interpretable, and training-free paradigm for generic and query-focused video summarization. Its success paves the way for more reliable deployment of large language models in practical video understanding applications, addressing key challenges of generalization, stability, and interpretability.
Deep Analysis
Background
随着视频内容在社交媒体、监控和在线教育中的快速增长，视频摘要成为内容管理的重要工具。早期方法主要依赖密集标注进行监督学习，取得了良好效果，但成本高昂且难以跨数据集泛化。无监督方法降低了标注需求，但难以捕获高层语义和叙事线索。近年来，基于大模型的零样本方法兴起，利用预训练的视觉-语言模型描述场景，再用大语言模型进行重要性评分，取得一定成果，但存在模板敏感和适应性差的问题。当前研究试图结合伪标签和结构化准则，提升模型稳定性和解释性，逐步突破这些瓶颈。
Core Problem
核心问题在于如何在缺乏大量标注的情况下，利用大模型实现稳定、准确的场景重要性评分。现有零样本方法对提示模板敏感，难以适应不同数据集和内容变化，导致评分不稳定。此外，缺乏结构化的评价准则，难以兼顾局部突出性和全局连贯性，限制了摘要质量和解释性。设计一种通用、稳定且具有可解释性的评分机制成为关键。
Innovation
本文提出结构化、多维评价准则结合伪标签的零样本框架。创新点包括：1）利用少量标注生成高置信度伪标签，抽象成多维准则；2）通过结构化提示引导大模型进行场景评分，减少模板敏感性；3）结合邻近场景的简要总结，平衡局部突出性与全局连贯性；4）建立“少量标注→伪标签→准则→场景评分→帧级摘要”的完整流程。该方法显著提升模型稳定性和可解释性，具备良好的跨域适应能力。
Methodology
- �� 场景划分：利用视觉特征变化检测场景边界，结合自适应阈值和短场景合并，确保场景语义完整；
- �� 描述生成：采用预训练视频-语言模型（如CLIP、VideoGPT）为每个场景生成详细描述，确保内容丰富；
- �� 伪标签构建：从少量标注中提取高置信度场景重要性，计算平均得分，形成伪标签；
- �� 评价准则抽象：用GPT-4进行原因挖掘，将高低重要场景的特征差异抽象成多维准则，形成结构化评分体系；
- �� 提示设计：基于准则，设计场景评分提示，包括边界场景（仅描述）和中间场景（邻近场景结合描述），引导大模型输出评分；
- �� 平滑与融合：将场景得分通过时间平滑转化为帧级重要性曲线，实现连续摘要。
Experiments
在SumMe、TVSum和QFVS三个公开数据集上验证，采用少量标注样本生成伪标签，比较不同提示策略和准则设计的效果。指标为F1-score，采用交叉验证确保稳定性。对比基线包括PROMPTS TO SUMMARIES、AC-SUM-GAN等。通过消融实验验证伪标签、准则结构和邻近场景融合的贡献，调优参数如场景划分阈值和描述生成长度。结果显示，本文方法在三个数据集上均优于零样本基线，表现出良好的稳定性和泛化能力。
Results
在SumMe数据集，F1达57.58，超越基线56.73，提升0.85；TVSum达63.05，超越62.21，提升0.84；QFVS达53.79，超53.42，提升0.37。伪标签和准则结构显著提升评分稳定性，邻近场景融合增强叙事连贯性。多维准则设计使模型更好捕获场景关键特征，减少模板敏感，提升跨域适应性。整体表现验证了方法的有效性和实用性。
Applications
该方法适用于视频内容管理、自动摘要、内容检索等场景，尤其在缺乏大量标注的情况下表现优异。可应用于社交媒体内容筛选、监控视频快速浏览、长视频内容梳理等。未来可结合用户偏好实现个性化摘要，推动智能内容生成和人机交互。
Limitations & Outlook
目前依赖少量标注样本，可能在极端场景或偏离训练分布时表现不足。场景划分和描述生成的质量直接影响评分效果，长视频推理成本较高。未来需优化模型效率，增强对复杂叙事和多模态信息的理解能力。
Plain Language Accessible to non-experts
想象你在整理一堆杂乱的照片，想快速挑出最重要的几张。传统方法像是每张照片都要有人逐一打分，费时又不灵活。现在，假设你有一些朋友帮你看过部分照片，告诉你哪些最精彩，哪些可以忽略。你把这些朋友的建议整理成一套简单的规则，然后用这个规则去评判剩下的照片。这样一来，不用每次都请朋友帮忙，你就能快速筛选出最精彩的照片。这就像本文的方法，用少量的人类标注作为“朋友”的建议，结合大模型的智能，自动评判视频中的场景，既节省时间，又保证效果稳定。通过结构化的规则和上下文信息，模型能更好理解视频内容，生成简洁、连贯的摘要，帮助我们更快找到想看的内容。
ELI14 Explained like you're 14
你知道在社交媒体上看到很多短视频吗？有时候视频太长，想快点知道内容重点。以前，要么自己花时间看，要么让很多人帮忙打分，成本很高。现在，有个聪明的办法：只用少量专家标注的例子，教会电脑怎么判断哪个场景重要，然后用这个“规则”去自动打分。这个方法还会考虑视频前后场景的关系，就像你看一段故事时，会记得前面的内容，理解后面的发展。这样一来，电脑可以快速总结出视频的核心内容，效果还很稳定，不会因为模板不同而出错。就像你用一套聪明的“评分规则”去筛选视频，既省事又靠谱，特别适合海量视频内容的管理和分享。
Glossary
伪标签 (Pseudo-Labels)
由模型根据少量标注自动生成的标签，用于指导训练或评估，减少对大量人工标注的依赖。
本文中用伪标签指导场景重要性评分，提升模型稳定性。
结构化评分准则 (Structured Rubrics)
多维评价体系，明确描述场景重要性、叙事连贯性等指标，增强模型的可解释性。
用以抽象场景特征，指导大模型评分。
上下文感知 (Context-Aware)
模型在评估时考虑邻近场景或全局信息，以提升判断的连贯性和准确性。
中间场景结合邻近场景描述，平衡局部与全局。
零样本学习 (Zero-Shot Learning)
模型无需特定训练，即能在新任务或数据上进行推理和判断的能力。
本文利用大模型实现无需训练的场景评分。
视频-语言模型 (Video-Language Model)
结合视觉和文本信息，理解视频内容并生成描述的深度模型。
用于生成场景描述和辅助评分。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端复杂场景下的表现，尤其是多模态信息融合和长时序理解能力仍待探索。现有方法在极长视频或多样内容中可能出现性能瓶颈，未来需要优化模型结构和推理效率。
Applications
Immediate Applications
视频内容管理
自动生成视频摘要，帮助用户快速浏览和筛选海量内容，提升内容检索效率。
监控视频分析
快速识别关键事件，支持安防和监控系统的智能化升级。
Long-term Vision
智能内容生成
结合用户偏好，生成个性化视频摘要和内容推荐，推动内容个性化和交互式体验。
Abstract
We propose a rubric-guided, pseudo-labeled, and prompt-driven zero-shot video summarization framework that bridges large language models with structured semantic reasoning. A small subset of human annotations is converted into high-confidence pseudo labels and organized into dataset-adaptive rubrics defining clear evaluation dimensions such as thematic relevance, action detail, and narrative progression. During inference, boundary scenes, including the opening and closing segments, are scored independently based on their own descriptions, while intermediate scenes incorporate concise summaries of adjacent segments to assess narrative continuity and redundancy. This design enables the language model to balance local salience with global coherence without any parameter tuning. Across three benchmarks, the proposed method achieves stable and competitive results, with F1 scores of 57.58 on SumMe, 63.05 on TVSum, and 53.79 on QFVS, surpassing zero-shot baselines by +0.85, +0.84, and +0.37, respectively. These outcomes demonstrate that rubric-guided pseudo labeling combined with contextual prompting effectively stabilizes LLM-based scoring and establishes a general, interpretable, and training-free paradigm for both generic and query-focused video summarization.