An Attribute-Based Measure of Video Complexity

The paper introduces Video Attribute-Based Complexity (VideoABC), a non‑parametric framework that estimates the likelihood of failure for video‑LLMs on video‑question pairs. VideoABC projects reference videos into a space defined by a pre‑defined vocabulary of video attributes (e.g., scene complexity, event speed), quantizes this space, and computes expected complexity values for each quantization cell. To handle small reference datasets, it combines a k‑means quantizer for in‑distribution samples with a universal lattice quantizer for out‑of‑distribution generalization, and uses a synthetic video generation procedure to populate lattice cells during training. Experiments demonstrate that VideoABC outperforms methods like ‘video‑LLM as judge’ while requiring fewer resources, and its attribute‑based scores provide interpretable insights into benchmark complexity.

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• llms
• diffusion
• efficiency
• benchmarks