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.
Recursive generalization transformer for image super-resolution
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2representative citing papers
citing papers explorer
-
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.
- Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration