Enhancing Visual Token Representations for Video Large Language Models via Training-Free Spatial-Temporal Pooling and Gridding

2024-01-30 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, extended

Summary

ST-GridPool is a novel training-free visual token enhancement method for Video Large Language Models (LLMs) that addresses challenges in efficiently compressing visual tokens while preserving spatiotemporal interactions. Developed by researchers from Tsinghua, Shenzhen, and Xidian Universities, ST-GridPool integrates Pyramid Temporal Gridding (PTG) for multi-grained spatiotemporal interaction capture and Norm-based Spatial Pooling (NSP) to prioritize high-information visual regions based on token norms. Experiments on LLaVA-OneVision-7B and LLaVA-Video-7B across 6 video understanding datasets, including VideoMME and LongVideoBench, demonstrate consistent performance improvements. The method significantly enhances long-term video understanding, outperforms existing 7B models on long-form benchmarks, and shows robustness under strict token budgets (e.g., 30% and 50% token budgets). It also substantially reduces inference time and peak GPU memory usage.

Key takeaway

For Machine Learning Engineers optimizing Video LLM performance and efficiency, you should consider integrating ST-GridPool as a plug-and-play enhancement. This training-free method significantly improves video understanding, especially for long-form content and under strict token budgets, while also reducing inference latency and GPU memory. Implementing ST-GridPool can boost your models like LLaVA-Video without costly retraining or architectural changes, making it a valuable addition for robust video analysis applications.

Key insights

ST-GridPool enhances Video LLM visual tokens via training-free hierarchical temporal gridding and norm-based spatial pooling for improved video understanding.

Principles

• Token norm correlates positively with visual semantic richness.
• Hierarchical temporal gridding captures multi-grained dynamics.
• Training-free methods can significantly enhance Video LLM performance.

Method

ST-GridPool combines Pyramid Temporal Gridding (PTG) for multi-scale temporal feature extraction and Norm-based Spatial Pooling (NSP) for dynamic spatial weighting based on token norms, then applies weighted summation.

In practice

• Apply ST-GridPool to LLaVA-Video-7B for long-form video tasks.
• Use L2-norm with β=1 for optimal spatial pooling.
• Consider a (2,2) kernel size for spatial pooling.

Topics

• Video Large Language Models
• Visual Token Compression
• Spatial-Temporal Pooling
• Pyramid Temporal Gridding
• Norm-based Spatial Pooling
• LLaVA-Video
• Computational Efficiency

Code references

• bingjunluo/ST-GridPool

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.