Researchers have developed new methods to prune visual tokens in large vision-language models (LVLMs), significantly reducing computational costs without substantial performance loss. The first approach, BtsPruner, identifies token roles during visual encoding and selectively preserves complementary local and global information. The second method, RI-Prune, specifically addresses multi-image LVLMs by using relation and information awareness, moving beyond attention-based saliency which proves ineffective in multi-image scenarios. Both techniques demonstrate improved efficiency and performance retention across various LVLM architectures. AI
IMPACT These pruning techniques could significantly reduce inference costs and enable wider deployment of complex vision-language models.
RANK_REASON Two research papers introduce novel methods for pruning visual tokens in large vision-language models.
Read on Hugging Face Daily Papers →
- BtsPruner
- CogVLM
- CUDA
- InternVL
- Large Vision Language Models
- LLaVA-1.5
- LLaVA-1.5-7B
- LLaVA-NeXT
- LLaVA-NeXT-7B
- Qwen VL
- arXiv
- Hugging Face
- LVLMs
- RI-Prune
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