MVEB: Massive Video Embedding Benchmark

2026-06-12 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Massive Video Embedding Benchmark (MVEB) introduces a 23-task evaluation suite for video embeddings, encompassing classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering. Evaluating 33 models, the benchmark reveals no single model dominates; MLLM-based embeddings excel in classification, clustering, pair classification, and QA, while multimodal binding leads in retrieval and zero-shot classification. A critical finding is that audio's contribution varies with dataset annotation provenance, showing a six-point performance gap: it helps when labels derive from both modalities but hurts when from visuals alone. MVEB, derived from a 184-task pool (MVEB+), integrates into the MTEB ecosystem, with all tasks, code, and a leaderboard released.

Key takeaway

For machine learning engineers developing video understanding systems, you should recognize that no single embedding model currently offers universal superiority across diverse tasks. When designing or selecting models, carefully consider the specific task requirements and the provenance of your training data's annotations, especially regarding audio integration, as it can significantly impact performance. Utilize the MVEB benchmark to rigorously evaluate and compare models for your specific use cases.

Key insights

The MVEB benchmark reveals diverse video embedding model performance, with no single architecture dominating across all 23 tasks.

Principles

• Audio's utility in video embeddings depends on label provenance.
• Generative MLLMs need contrastive adaptation for cross-modal tasks.
• No single video embedding model excels universally.

Method

MVEB provides a 23-task benchmark for video embeddings, derived from a 184-task pool, integrating into the MTEB ecosystem for unified evaluation across modalities.

In practice

• Evaluate video embeddings across diverse tasks using MVEB.
• Consider label provenance when integrating audio into video models.
• Adapt generative MLLMs for cross-modal video tasks.

Topics

• Video Embeddings
• Multimodal Benchmarking
• MLLMs
• Zero-shot Learning
• Video Retrieval
• Audio-Visual Models

Code references

• embeddings-benchmark/mteb

Best for: Research Scientist, AI Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.