CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

CoPE-VideoLM: A Breakthrough in Video Language Model Efficiency

A new approach in video language models, CoPE-VideoLM, enhances AI systems' understanding of video content by utilizing video codec primitives. This method addresses challenges faced by current Video Language Models (VideoLMs), such as high computational costs and limited temporal coverage.

Traditional video analysis often relies on keyframe sampling, which can overlook critical events and details. CoPE-VideoLM mitigates these issues by utilizing motion vectors and residuals from video codecs, allowing for efficient representation without full-image encoding for most frames.

Innovative Transformer-Based Encoders

The CoPE-VideoLM framework introduces lightweight transformer-based encoders that aggregate codec primitives. This approach improves efficiency in end-to-end fine-tuning, achieving a reduction in time-to-first-token by up to 86% and reducing token usage by as much as 93% compared to standard VideoLMs.

Performance Across Diverse Benchmarks

CoPE-VideoLM maintains or surpasses performance on 14 diverse video understanding benchmarks, including tasks related to general question answering, temporal reasoning, and spatial scene understanding. This demonstrates the versatility of the CoPE-VideoLM approach while maintaining performance across various video analysis tasks.