ARTA: Adaptive Mixed-Resolution Token Allocation for Efficient Dense Feature Extraction
📰 ArXiv cs.AI
ARTA is a vision transformer that efficiently extracts dense features using adaptive mixed-resolution token allocation
Action Steps
- Start with low-resolution tokens and use a lightweight allocator to predict regions requiring more fine tokens
- Iteratively predict semantic boundary scores and allocate additional tokens to patches above a low threshold
- Refine token allocation through multiple iterations to achieve efficient dense feature extraction
Who Needs to Know This
Computer vision engineers and researchers on a team can benefit from ARTA as it improves the efficiency of dense feature extraction, while product managers can consider its potential for applications in image and video analysis
Key Insight
💡 Adaptive token allocation can significantly improve the efficiency of dense feature extraction in vision transformers
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💡 Efficient dense feature extraction with ARTA, a mixed-resolution vision transformer
Key Takeaways
ARTA is a vision transformer that efficiently extracts dense features using adaptive mixed-resolution token allocation
Full Article
Title: ARTA: Adaptive Mixed-Resolution Token Allocation for Efficient Dense Feature Extraction
Abstract:
arXiv:2603.26258v1 Announce Type: cross Abstract: We present ARTA, a mixed-resolution coarse-to-fine vision transformer for efficient dense feature extraction. Unlike models that begin with dense high-resolution (fine) tokens, ARTA starts with low-resolution (coarse) tokens and uses a lightweight allocator to predict which regions require more fine tokens. The allocator iteratively predicts a semantic (class) boundary score and allocates additional tokens to patches above a low threshold, concen
Abstract:
arXiv:2603.26258v1 Announce Type: cross Abstract: We present ARTA, a mixed-resolution coarse-to-fine vision transformer for efficient dense feature extraction. Unlike models that begin with dense high-resolution (fine) tokens, ARTA starts with low-resolution (coarse) tokens and uses a lightweight allocator to predict which regions require more fine tokens. The allocator iteratively predicts a semantic (class) boundary score and allocates additional tokens to patches above a low threshold, concen
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