MVEB: Massive Video Embedding Benchmark
MVEB benchmarks 23 tasks across 33 models, revealing diverse strengths and limitations in multi-modal video embeddings.
Key Findings
Methodology
MVEB constructs a curated 23-task subset from the 184-task MVEB+ pool, based on correlation and efficiency criteria. It evaluates models across six families: MLLM, contrastive, multimodal binding, and generative, using zero-shot metrics. Paired evaluations compare video-only and audio-video inputs, analyzing how modality influences performance. Metrics include accuracy, V-measure, nDCG, and ranking via Borda count, ensuring comprehensive assessment of task diversity and model robustness.
Key Results
- No single model dominates; MLLM embeddings excel in classification, clustering, pairwise tasks, and QA; multimodal binding models lead in retrieval and zero-shot classification; generative MLLMs without contrastive adaptation underperform in cross-modal tasks. Audio improves performance on datasets with multi-modal labels (+6 points) but hampers it on visual-only labels, highlighting label source impact.
- Model rankings show LCO-Embedding-Omni-7B at the top overall, with Qwen3-VL-Embedding leading in text-video tasks. Performance correlates weakly with parameter size; training data alignment and contrastive learning are more critical. Pareto frontiers reveal that smaller contrastive models outperform larger generative models in many tasks.
- Analysis indicates that training data modality alignment and contrastive stages are essential for cross-modal performance. Clustering and zero-shot scores remain modest (~27 points), but overall trends favor multimodal fusion and contrastive learning as future directions.
Significance
MVEB addresses the fragmentation in video embedding evaluation by providing a unified, multi-task benchmark. It emphasizes the importance of multimodal data integration and task diversity, facilitating the development of general-purpose video representations. This benchmark supports industry and academia in building more robust, versatile models capable of understanding complex video content, thus accelerating applications like automated captioning, content filtering, and intelligent video retrieval. Its community-driven design ensures continuous updates, fostering collaborative progress in multimodal video understanding.
Technical Contribution
The core innovation lies in integrating 184 tasks into a curated 23-task benchmark, emphasizing task diversity and efficiency. It systematically compares unimodal and multimodal inputs, highlighting the importance of contrastive learning stages. The evaluation framework employs Borda ranking for fair model comparison across multiple tasks, promoting reproducibility and community participation. The benchmark's design encourages the development of models that excel across diverse tasks and modalities, setting a new standard for comprehensive video embedding evaluation.
Novelty
This is the first large-scale, multi-task, multi-modal video embedding benchmark that combines extensive task filtering, systematic modality comparison, and a community-maintained evaluation ecosystem. It uniquely reveals the impact of label provenance on model performance and underscores the necessity of contrastive pretraining for cross-modal tasks. These innovations provide a new paradigm for evaluating and developing general-purpose video representations, surpassing prior efforts that focused on narrow tasks or single modalities.
Limitations
- Current evaluation emphasizes static videos and limited task types; dynamic interactions and long videos are underrepresented, limiting real-world applicability. The high computational cost of training large models restricts widespread adoption. Variability in dataset labels and sources affects generalization, requiring more robust data curation. Cross-modal fusion performance still lags in complex scenarios, necessitating further research into scalable, robust multimodal training strategies.
Future Work
Future directions include expanding task diversity to cover dynamic and interactive videos, optimizing training efficiency for large models, and improving label quality and consistency. Developing more scalable contrastive learning algorithms and exploring advanced multimodal fusion techniques will be prioritized. Additionally, extending the benchmark to include long videos, multi-turn question answering, and real-time understanding will further enhance its practical relevance. Community collaboration and continuous updates will ensure MVEB remains a leading standard for multimodal video embedding evaluation.
AI Executive Summary
MVEB represents a significant advancement in the evaluation of general-purpose video embeddings, addressing the fragmentation and limited scope of prior benchmarks. By curating a diverse set of 23 representative tasks from a larger pool of 184, it ensures comprehensive assessment across classification, retrieval, clustering, and question answering. The benchmark evaluates a broad spectrum of models, including large multimodal language models (MLLMs), contrastive, binding, and generative architectures, revealing nuanced performance patterns.
The findings demonstrate that no single model dominates all tasks. MLLM-based embeddings excel in classification and QA, while multimodal binding models outperform in retrieval and zero-shot tasks. Importantly, the study highlights the critical role of training data alignment and contrastive learning stages, with audio modality contributing positively when labels are multi-modal but negatively when labels are visual-only. These insights guide future model development, emphasizing the importance of data quality and training strategies.
The MVEB benchmark not only provides a standardized, community-maintained platform for evaluating multi-task, multi-modal video models but also fosters innovation by revealing key factors influencing performance. Its design ensures efficiency, scalability, and fairness, making it a valuable tool for academia and industry. Looking ahead, expanding task diversity, optimizing training methods, and enhancing multimodal fusion techniques will be crucial for advancing the state of the art. Overall, MVEB paves the way for more robust, versatile, and intelligent video understanding systems, with broad implications for applications ranging from content moderation to autonomous systems.
Deep Analysis
Background
Video representation技术经历了从单一视觉特征到多模态融合的演变。早期如C3D、I3D等模型专注于动作识别，随后出现视频检索和问答任务。近年来，深度学习模型如X-CLIP、V-JEPA、Perception Encoder等推动了多模态学习，强调对比学习和多模态对齐。现有基准如VideoEval、UVRB、VidVec等多偏重单一任务或模态，缺乏统一多任务评估体系。MVEB借鉴MTEB等多模态基准，结合184任务池，旨在实现多任务、多模态的全面评估，推动通用视频表示的发展。
Core Problem
现有视频嵌入模型在多任务、多模态场景中的表现差异大，缺乏统一评估标准，难以衡量模型的泛化能力。多模态融合的有效性依赖于训练数据的模态对齐，模型在不同任务中的表现不一致，尤其在跨模态检索和问答中表现不足。此外，缺少考虑标签来源和任务多样性的评估体系，限制了模型的实际应用潜力。如何设计一个兼顾多任务、多模态、多数据源的评估平台，成为行业亟待解决的问题。
Innovation
MVEB的核心创新在于：1）结合184任务池，筛选出具有代表性的23任务，确保任务多样性与评估效率；2）系统性比较视频单模态与音频视频融合效果，揭示标签来源对模型性能的影响；3）强调对比学习在跨模态任务中的关键作用，提出多模态对比学习的必要性；4）采用Borda计数排名机制，确保评估的公平性与连续性。这些创新突破了以往单一任务或模态的局限，为多模态视频理解提供了系统化的评估框架。
Methodology
- �� 数据采集：筛选多源、多场景、多模态数据，确保多样性和代表性。• 任务设计：涵盖分类、零样本、聚类、配对、检索、问答六大类，确保任务多样性。• 任务筛选：基于相关性、效率和覆盖原则，从184任务中筛选23个代表性任务。• 模型评估：涵盖MLLM、对比学习、多模态绑定和生成模型，采用零样本评估，无需微调。• 输入配置：支持视频、音频、文本多模态输入，比较视频单模态与音频视频融合效果。• 评价指标：准确率、V-measure、nDCG等，分析模型在不同任务和模态中的表现差异。
Experiments
- �� 数据集：包括ActionNet、MSR-VTT、Kinetics等多源数据。• 模型：评估33个公开模型，参数规模从200M到10.7B。• 训练：采用对比学习、模态对齐、少样本调优等策略。• 评估：在不同任务和模态组合下，测量准确率、排名指标。• 统计：分析模型排名、参数与性能关系，验证多模态融合的重要性。
Results
- �� 无单一模型在所有任务中占优，MLLM嵌入在分类和问答中表现优异；多模态绑定在检索中领先。• 音频在多模态标签中提升性能（+6个百分点），在单模态标签中反而降低表现，显示标签来源影响显著。• 参数规模与性能关系复杂，训练数据的模态对齐和对比学习更关键。• 低分任务如聚类和零样本分类仍有提升空间，但整体趋势显示多模态融合和对比学习是未来重点。
Applications
- �� 实时视频分析：支持智能监控、内容筛查等场景，提升识别准确性。• 内容检索：实现跨模态检索，便于多媒体内容管理。• 未来智能视频：推动自动问答、场景理解、交互式视频生成等应用，依赖多模态深度融合。
Limitations & Outlook
- �� 评估主要集中静态视频，动态交互和长视频场景不足。• 模型训练成本高，尤其是大规模MLLM，限制推广。• 标签来源差异影响模型泛化，数据偏差需优化。• 跨模态融合在极端场景中表现仍有限，需持续研究改进。
Plain Language Accessible to non-experts
想象你在一家大型工厂，工厂里有很多不同的机器，每台机器负责不同的任务。有的机器专门识别动作，有的负责理解图片，有的能听声音。以前这些机器各自工作，不能互通。现在，科学家们设计了一种新方法，就像给所有机器装上了相通的“语言”，让它们可以一起合作完成更复杂的任务，比如识别视频内容、回答问题甚至检索信息。这个方法叫MVEB，就像给工厂装上了智能大脑，能同时处理多种任务。它通过筛选大量的任务和数据，让每台机器都变得更聪明、更合作。这样一来，无论是识别动作、理解场景，还是听声音，都能变得更快更准。未来，这个系统还能帮助我们更好地理解视频内容，比如自动生成字幕、智能推荐视频，甚至让机器人更聪明。就像工厂里的每台机器都变得更智能，整个工厂的效率也会大大提升。
ELI14 Explained like you're 14
想象你在玩一个超级酷的游戏，你的任务是识别视频里的动作、回答关于视频的问题，或者找到相似的视频。以前，每个任务都需要用不同的工具，效率很低。而现在，有一种叫MVEB的“超级工具箱”，它可以同时帮你完成很多任务。这个工具箱里有很多“机器人”，每个都擅长不同的事情，比如有的专门看视频动作，有的能听声音，还有的能理解文字。科学家们让这些“机器人”学会合作，就像你和朋友一起玩游戏一样。它们通过学习大量的视频和声音，变得越来越聪明。比如，某个模型可以在没有训练的情况下，直接告诉你视频里是不是有人在跳舞，或者找到和你喜欢的音乐配的视频。这个方法让机器变得更像人一样聪明，能理解复杂的视频内容。未来，这样的技术可以用在智能助手、自动字幕、视频搜索等很多地方，让我们的生活变得更方便、更有趣。
Glossary
Multimodal Learning (多模态学习)
结合多种数据类型（如视频、声音、文字）进行学习，提升模型理解能力。
MVEB评估中，强调视频与音频、文本的融合效果。
Contrastive Learning (对比学习)
通过拉近相关样本距离、拉远不相关样本距离，增强模型的判别能力。
多模态嵌入模型中的核心训练机制。
Borda Count (Borda计数)
一种排序投票方法，将多个模型的排名合成为总体排名。
MVEB中用于模型整体排名。
Zero-shot Classification (零样本分类)
模型无需微调，直接根据标签描述进行分类。
MVEB评估模型泛化能力的重要指标。
Multimodal Binding (多模态绑定)
将不同模态（视频、音频、文本）映射到统一空间，实现跨模态理解。
评估模型在多模态融合中的表现。
Open Questions Unanswered questions from this research
- 1 如何提升模型在长视频和复杂场景中的理解能力，仍是未来研究重点。
- 2 多模态标签的质量和一致性对模型性能影响巨大，需优化数据采集与标注策略。
- 3 跨模态对比学习的效率与鲁棒性有待提升，特别是在极端环境下的表现。
Applications
Immediate Applications
Multimodal Video Retrieval
支持内容管理、视频搜索，提升检索速度和准确性，依赖高质量多模态嵌入模型。
Automatic Content Analysis
在监控、内容审核中实现自动识别与分类，减少人工成本，提高效率。
Long-term Vision
Intelligent Video Understanding System
未来实现全场景、多模态、实时视频理解，支持自动生成字幕、场景描述、交互式内容生成。
Abstract
We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering. We evaluate 33 models and find that no single model dominates: MLLM-based embeddings lead on classification, clustering, pair classification, and QA; multimodal binding leads on retrieval and zero-shot classification; generative MLLMs without contrastive adaptation collapse on cross-modal tasks. Paired video-only vs. audio+video evaluations show that audio's contribution depends on dataset annotation provenance: audio helps when labels were produced from both modalities and hurts when they were produced from visuals alone, a six-point gap consistent across model families. MVEB is derived from MVEB+, a 184-task pool, and is designed to maintain task diversity while reducing evaluation cost. It integrates into the MTEB ecosystem for unified evaluation across text, image, audio, and video. We release MVEB and all 184 tasks along with code and a leaderboard at https://github.com/embeddings-benchmark/mteb.