ST-SimDiff: Balancing Spatiotemporal Similarity and Difference for Efficient Video Understanding with MLLMs

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

Summary

The ST-SimDiff framework addresses the computational inefficiency of Multimodal Large Language Models (MLLMs) when processing long videos by balancing spatiotemporal similarity and difference. This training-free method constructs a spatio-temporal graph from visual tokens to model complex associations. It employs a dual-selection strategy: similarity-based selection uses community detection to compress static information by retaining representative tokens, while temporal difference-based selection identifies and preserves tokens at key content-changing points. Experiments on LLaVA-Video-7B, NVILA-8B, and Qwen2.5-VL-7B across benchmarks like VideoMME and LongVideoBench demonstrate ST-SimDiff's superior performance. It achieves up to 30.2% inference time savings and 31.7% GPU memory reduction for 128-frame videos, often matching or exceeding uncompressed model performance at 30% and 50% token retention ratios.

Key takeaway

For Machine Learning Engineers deploying Multimodal Large Language Models for video understanding, if you are struggling with prohibitive computational and memory costs for long videos, you should consider ST-SimDiff. This training-free framework significantly reduces visual token overhead by intelligently balancing redundancy compression with critical event preservation. Implementing ST-SimDiff can lead to substantial inference time and GPU memory savings, enabling more efficient processing of extended video content on existing hardware.

Key insights

Efficient video understanding balances compressing redundant content with preserving dynamic, event-driven changes.

Principles

• Spatio-temporal graphs uniformly model complex token associations.
• Similarity identifies redundancy for compression.
• Difference captures key dynamic events.

Method

Construct a spatio-temporal graph. Apply parallel similarity-based community detection for representative tokens and temporal difference-based selection for event tokens. Merge and prune by attention score.

In practice

• Implement graph-based community detection for static content.
• Utilize temporal similarity drops to pinpoint event tokens.
• Combine dual selection with attention-based pruning.

Topics

• Multimodal LLMs
• Video Understanding
• Token Compression
• Spatio-Temporal Graphs
• Community Detection
• Computational Efficiency

Code references

• bingjunluo/ST-SimDiff

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.