CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

2026-02-13 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

CoPE-VideoLM introduces an efficient approach for Video Language Models (VideoLMs) by utilizing video codec primitives, specifically motion vectors and residuals, to overcome the limitations of keyframe sampling. Current VideoLMs often miss crucial temporal details and incur high computational costs due to processing full images. CoPE-VideoLM addresses this by natively encoding video redundancy and sparsity, eliminating the need for expensive full-image encoding for most frames. The system employs lightweight transformer-based encoders to aggregate these codec primitives and aligns their representations with image encoder embeddings via a pre-training strategy. This method significantly reduces the time-to-first-token by up to 86% and token usage by up to 93%, while maintaining or improving performance across 14 diverse video understanding benchmarks, including general question answering and long-form understanding.

Key takeaway

For research scientists developing Video Language Models, CoPE-VideoLM offers a compelling alternative to traditional keyframe sampling. Your models can achieve substantial efficiency gains, reducing time-to-first-token by up to 86% and token usage by 93%, without sacrificing performance on diverse video understanding tasks. Consider integrating codec primitives into your pre-training strategies to enhance temporal dynamics and scalability.

Key insights

CoPE-VideoLM uses video codec primitives to enhance VideoLM efficiency and temporal understanding.

Principles

• Leverage native video redundancy encoding.
• Align primitive representations with image embeddings.

Method

Aggregate motion vectors and residuals with lightweight transformer encoders, then align representations with image encoder embeddings through pre-training for end-to-end fine-tuning.

In practice

• Reduce VideoLM inference time by 86%.
• Decrease token usage by 93%.
• Improve temporal reasoning in VideoLMs.

Topics

• Video Language Models
• Codec Primitives
• Video Understanding
• Computational Efficiency
• Transformer Encoders

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Deep Learning Engineer

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