arXiv:2606.14958v1 Announce Type: cross 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
The proliferation of massive multimodal datasets and advanced generative models has created a critical need for standardized benchmarks to assess video embedding capabilities.
A comprehensive benchmark like MVEB is crucial for guiding research and development in video AI, directly impacting the capabilities of future AI systems to understand and process visual information.
The explicit identification of strengths and weaknesses across different video embedding architectures will accelerate targeted improvements and potentially lead to more versatile and robust video AI models.
- · AI research labs
- · Video analytics companies
- · Generative AI developers
- · Multimodal AI developers
- · Monolithic single-purpose AI models
Improved video understanding models across various applications, from content creation to surveillance.
Faster development and deployment of sophisticated AI agents capable of interpreting and interacting with video data.
Enhanced societal integration of AI systems due to their increased ability to perceive and reason about the visual world.
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Read at arXiv cs.LG