Research Paper#Vision Transformers, Token Reduction, Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 16:21
Neighbor-Aware Token Reduction for Efficient Vision Transformers
Published:Dec 28, 2025 03:25
•1 min read
•ArXiv
Analysis
This paper addresses the computational inefficiency of Vision Transformers (ViTs) due to redundant token representations. It proposes a novel approach using Hilbert curve reordering to preserve spatial continuity and neighbor relationships, which are often overlooked by existing token reduction methods. The introduction of Neighbor-Aware Pruning (NAP) and Merging by Adjacent Token similarity (MAT) are key contributions, leading to improved accuracy-efficiency trade-offs. The work emphasizes the importance of spatial context in ViT optimization.
Key Takeaways
- •Addresses computational inefficiency in Vision Transformers.
- •Introduces neighbor-aware token reduction using Hilbert curve reordering.
- •Proposes Neighbor-Aware Pruning (NAP) and Merging by Adjacent Token similarity (MAT).
- •Achieves improved accuracy-efficiency trade-offs.
- •Highlights the importance of spatial continuity and neighbor structure in ViTs.
Reference
“The paper proposes novel neighbor-aware token reduction methods based on Hilbert curve reordering, which explicitly preserves the neighbor structure in a 2D space using 1D sequential representations.”