TTST: A Top- k Token Selective Transformer for Remote Sensing Image Super-Resolution
Wuhan University · Harbin Institute of Technology · +2 more institutions
Abstract
Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens; (2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the…
Citation impact
- FWCI
- 70.01
- Percentile
- 100%
- References
- 72
Authors
6Topics & keywords
- Security token
- Computer science
- Transformer
- Artificial intelligence
- Pattern recognition (psychology)
- Voltage
- Engineering
- Computer network