New SPLIT method speeds up Vision Transformers by learning token resolution

Researchers have developed a new method called Split Policy Learned Image Tokenization (SPLIT) to improve the efficiency of Vision Transformers (ViTs). SPLIT learns to allocate token resolution based on the model's interpretation of task-relevant information, rather than relying on raw pixel statistics. This approach results in shorter token sequences that maintain accuracy, achieving competitive results on classification tasks and dense prediction tasks like COCO and ADE20K while using up to 30% fewer tokens. AI

IMPACT This method could lead to faster and more efficient AI models for image analysis tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for improving AI model efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• ADE20K
• arXiv
• COCO
• Hugging Face
• Split Policy Learned Image Tokenization
• Vision Transformers

• paper
• infra

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