FastVGGT achieves 4x speedup on VGGT for 1000-image inputs using training-free token merging tailored to 3D architectures while reducing error accumulation.
Which tokens to use? investi- gating token reduction in vision transformers
2 Pith papers cite this work. Polarity classification is still indexing.
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SAT introduces density and isolation-based token aggregation to enable efficient global attention in super-resolution transformers, claiming up to 0.22 dB PSNR gain and 27% FLOP reduction over PFT.
citing papers explorer
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FastVGGT: Training-Free Acceleration of Visual Geometry Transformer
FastVGGT achieves 4x speedup on VGGT for 1000-image inputs using training-free token merging tailored to 3D architectures while reducing error accumulation.
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SAT: Selective Aggregation Transformer for Image Super-Resolution
SAT introduces density and isolation-based token aggregation to enable efficient global attention in super-resolution transformers, claiming up to 0.22 dB PSNR gain and 27% FLOP reduction over PFT.