CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

• URL: http://arxiv.org/abs/2602.13191v1
• Date: Fri, 13 Feb 2026 18:57:31 GMT
• Title: CoPE-VideoLM: Codec Primitives For Efficient Video Language Models
• Authors: Sayan Deb Sarkar, Rémi Pautrat, Ondrej Miksik, Marc Pollefeys, Iro Armeni, Mahdi Rad, Mihai Dusmanu,
• Abstract summary: Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos.<n>Current methods use sampling which can miss both macro-level events and micro-level details due to the sparse temporal coverage.<n>We propose to leverage video primitives which encode video redundancy and sparsity without requiring expensive full-image encoding for most frames.
• Score: 56.76440182038839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, current methods use keyframe sampling which can miss both macro-level events and micro-level details due to the sparse temporal coverage. Furthermore, processing full images and their tokens for each frame incurs substantial computational overhead. To address these limitations, we propose to leverage video codec primitives (specifically motion vectors and residuals) which natively encode video redundancy and sparsity without requiring expensive full-image encoding for most frames. To this end, we introduce lightweight transformer-based encoders that aggregate codec primitives and align their representations with image encoder embeddings through a pre-training strategy that accelerates convergence during end-to-end fine-tuning. Our approach reduces the time-to-first-token by up to $86\%$ and token usage by up to $93\%$ compared to standard VideoLMs. Moreover, by varying the keyframe and codec primitive densities we are able to maintain or exceed performance on $14$ diverse video understanding benchmarks spanning general question answering, temporal reasoning, long-form understanding, and spatial scene understanding.

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