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Stateful Token Reduction for Long-Video Hybrid VLMs
arXiv – CS AI|Jindong Jiang, Amala Sanjay Deshmukh, Kateryna Chumachenko, Karan Sapra, Zhiding Yu, Guilin Liu, Andrew Tao, Pavlo Molchanov, Jan Kautz, Wonmin Byeon||6 views
🤖AI Summary
Researchers developed a new token reduction method for hybrid vision-language models that process long videos, achieving 3.8-4.2x speedup while retaining only 25% of visual tokens. The approach uses progressive reduction and unified scoring for both attention and Mamba blocks, maintaining near-baseline accuracy on long-context video benchmarks.
Key Takeaways
- →New token reduction method specifically designed for hybrid video vision-language models with attention and state-space blocks
- →Achieves 3.8-4.2x prefilling speedup while retaining only 25% of visual tokens
- →Introduces progressive low-to-high reduction schedule to address changing token importance across layers
- →Develops unified language-aware scoring mechanism for both attention and Mamba blocks
- →Maintains near-baseline accuracy on long-context video benchmarks with light finetuning
#vision-language-models#token-reduction#video-processing#mamba#transformer-optimization#hybrid-architectures#acceleration#long-context
Read Original →via arXiv – CS AI
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