Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models
arXiv:2604.11530v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information. Yet, they face significant challenges due to the high computational and memory demands of processing long sequences of vision tokens. Many existing methods rely on local heuristics, such as attention scores or token norms. However, these criteria suffer from positional bias and information dispersion, limiting their ability to preserve essential content at high pruning ratios and leading to performance degradation
The increasing computational demands of Vision-Language Models necessitate more efficient processing methods to scale their capabilities and deployment.
Improving the efficiency of Vision-Language Models addresses a crucial bottleneck in AI development, potentially enabling more powerful and accessible multi-modal AI applications.
This new method offers a more robust and less biased approach to vision token pruning, leading to better performance preservation at higher compression rates compared to existing techniques.
- · AI developers
- · Cloud providers
- · VLM-dependent applications
- · Edge AI computing
- · Less efficient VLM architectures
Reduced computational and memory footprint for training and inference of Vision-Language Models.
Accelerated development and wider adoption of complex multi-modal AI systems due to increased efficiency.
Lower barriers to entry for deploying sophisticated Vision-Language Models on resource-constrained devices, democratizing advanced AI.
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Read at arXiv cs.AI