arXiv:2606.02161v1 Announce Type: cross Abstract: Video Large Language Models (Video-LLMs) achieve strong performance in video understanding, but their excessive visual tokens bring substantial computational overhead. Existing training-free compression methods improve inference efficiency by reducing visual tokens, yet they often rely on local adjacent-frame similarity for temporal redundancy estimation or allocate token budgets mainly according to segment length. Such designs are sensitive to frame-level noise and fail to capture the non-uniform information distribution of real-world videos.
The rapid development and widespread adoption of Video-LLMs are creating urgent demand for improved efficiency, driving research into token compression techniques like InfoMerge.
Improving the efficiency of Video-LLMs addresses critical computational overheads, which are a major bottleneck for scaling and deploying these powerful models in real-world applications.
New methods like InfoMerge offer more sophisticated, information-aware approaches to token compression for Video-LLMs, moving beyond simpler spatial or temporal similarity-based techniques.
- · AI developers
- · Cloud computing providers
- · Content creation platforms
- · Edge AI device manufacturers
- · Inefficient video processing models
Video-LLMs become more economically viable and scalable due to reduced computational costs.
Broader deployment of Video-LLMs across various industries, including surveillance, entertainment, and robotics, becomes feasible.
Increased accessibility and integration of advanced video understanding capabilities could accelerate the development of autonomous AI agents interacting with real-world visual data.
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Read at arXiv cs.CL